Early prioritization prototype

Prioritizing papers in legal scholarship

we could first build a public candidate list

high-impact research-candidates workflow

address

safety

Practical applicability and influence

or future legal scholarship?

further public review has little expected value.

Publish citable evaluations and ratings, with evaluator anonymity optional.

nominated

student-edited

prestige law reviews

especially

Working papers, workshop drafts, and preprints could benefit from fast structured feedback before journal placement, policy uptake, or citation in public debate.

could add an independent quality signal.

Scholarship can shape legislation, court reasoning, agency action, policy design, and what future lawyers learn to treat as authoritative.

experts

, and improve it.

legal research

Impactful legal scholarship evaluation A concise public status and planning page for reviving structured expert evaluation of legal research in areas such as AI governance, biosecurity, animal welfare, and global health.

or open the direct annotation view.

Product label + independent confirmation from: Food Navigator, Bloomberg Intelligence, SPINS/NielsenIQ, or academic publication

pre-registered study is published

SV inventories,

Give both tools the same one-hour task: create a sample template_map.csv from three approved, non-confidential documents and produce a lawyer-readable Markdown summary. Compare setup friction, edit quality, citations to source files, and how easy it is to resume the work later.

Unjournal Pivotal Questions — Annotate this page via Hypothes.is (select any text to comment). This working note assesses whether current PBM market evidence is informative enough to study substitution and welfare impact — input to the workshop's research-value question. Produced by David Reinstein with iterative AI prompts.

hicken, shrimp, fish, beef, pork, eggs — since welfare weights differ sharply across them

(c) which animal types bear that displacement and in what proportions,

supply

make it the most tractable

plant-based

This is a relatively lenient threshold (a product can be marginally worse for the median consumer and still cross it

Market-share premium: categories with better average taste vs. worse-tasting categories²⁴ 10× more market share

Net assessment

The strongest indicator for self-consumption is the lapsed-buyer taste data:

Survey wording

consuming

Price evidence

How to read the evidence for the Pivotal Question

This supports price as a live lever, but it is not a field elasticity estimate.

finds

his is about the information content of share data — not an argument against studying displacement rates directly through scanner panels or field experiments

he retail market-share, consumer-panel, and taste-comparability data compiled here give partial evidence on term (a). They give weak or no direct evidence on (b), (c), or (d).

but price is the most studied and arguably the most tractable intervention

d the consumer surveys that establish omnivore dominance do not ask whether the purchase was for the respondent's own

within

Core finding

the natural channel reaches ~8%

The welfare mapping is unresolved

It does not prove future price/quality interventions fai

Household-panel evidence weakens a clean substitution story.

Market share is at best a ceiling on displacement.

Foodservice data prevents an overly retail-only view

Rügenwalder's reported category role,

is hypothesis-generating

ta is informative

nd mostly about ground products.

. The most direct evidence

low

current public data gives partial evidence

displacement ratio × displaced animal mix

Context; not direct share data

Consumer surveys and purchase panels consistently show that the large majority of PBM buyers are omnivores or flexitarians who also purchase conventional meat. The category is not, and has never been, predominantly a vegetarian market.

households would have bought on the specific PB occasions.

Rough conventional meat + aquatic-animal food flow, before a retail price conversion³¹³²

or an added eating occasion?

No public source here gives a like-for-like global conventional retail denominator. A rough calibration is still possible: 365 Mt of global meat plus roughly 165 Mt of aquatic animals used for direct human consumption implies about 530 Mt of conventional animal-product flow before retail conversion. At illustrative retail-equivalent prices of $3, $5, and $8/kg, the denominator would be about $1.6T, $2.7T, and $4.2T, making the $6.6B PB meat/seafood category roughly 0.4%, 0.25%, or 0.16% of that broad scale. This is not a matched market-share estimate.

s where most of the value and animal-suffering of conventional meat sits,

Current PB buyers are mostly dual-buyers, not substituters. GFI/SPINS data shows 96% of US plant-based meat buyer households also buy conventional meat, and they buy conventional meat far more frequently. Plant-based meat is functioning as an addition to existing diets in most cases, not a replacement. This complicates the welfare arithmetic: each dollar of PB sold may displace much less than a dollar of conventional.

Trajectories from current data may be misleading for projecting future adoption with better products.

Quality at parity hasn't unlocked majority adoption. Plant-based nuggets — the format that has reached sensory parity in blinded testing — still hold only 2 to 3% of the conventional nugget category. If matching taste isn't sufficient, then taste investments alone may have lower returns than the parity-headroom argument suggests.

Foodservice growth is real where products work. EU plant-based burger servings grew 90% from 2019 to 2023 in the Big 5 according to Circana. In channels where the product fits the use case and the price gap is hidden in menu pricing, adoption looks very different from retail. This is itself a quality-of-format finding.

because most PB buyers are dual-buyers, but the category is not literally too small to matter.

material

evidence that taste is one binding constraint.

A frank attempt to engage rather than defer.

NECTAR 2024 sensory study: category-level parity with conventional²⁰ Nuggets only

closest international analogu

but the cleanest topline is not the 6 to 7% US patty figure; it is the combination of low overall share with selective format-level strength and partial taste parity.

Public format-level summaries suggest much higher penetration in a few reformed categorie

Germany at 3.1% with similar product quality to the US's 1.4% shows that non-product factors (sausage culture, retailer strategy, private-label investment)

If treated as approximately 1:1 substitution

Roughly 9% of US households tried plant-based meat and stopped.

bratwurst

NECTAR sensory study: PB formats reaching parity with conventional²⁰

n the US it reaches 6 to 7% of conventional packaged patty dollar sales². The category-average understates penetration in the formats where taste and texture gaps are smallest, but the patty figure is itself an overstatement of plant-based meat's share of all hamburger consumption (see caveat below).

The sceptical concerns are partially but not fully supported. Overall share is genuinely low (US retail: 1.4%; Germany: 3.1%) and most products lag on taste. But three patterns make the evidence more informative than a simple dismissal implies: within the US, channel-level penetration varies widely — natural/specialty retailers such as Whole Foods reach ~8% of packaged-meat dollars vs 1.4% in mainstream multi-outlet retail; most buyers are omnivores or flexitarians rather than prior veg*ns, though whether they buy for their own consumption or as proxy for a veg*n household member remains open (§04.5); and better-tasting product categories capture 10× more market share, suggesting taste improvements have measurable adoption effects. Whether these patterns extrapolate to higher-quality, larger-market conditions is the central unresolved question.

versus 1.4% in mainstream multi-outlet.

A quick take: The sceptical concerns are only partially supported

raised

1

Each footnote in the dashboard links to a numbered row here. The full quote (or specific evidence) is shown in italics. URLs are direct links to the cited page or PDF where available.

here someone has published

, products in better-tasting categories already c

giving enough structural variation to analyse what drives adoption.

hese are genuinely low figures

The

The key open question is whether these patterns extrapolate to better-quality conditions.

in formats, channels, and geographies to study?

Workshop

r: how much do plant-based products actually replace animal products? This is the focus of The Unjournal's Plant-Based Substitution Pivotal Question.

Background note: a first-pass Claude summary of evidence on PBA penetration and taste-comparability is available for sharing. It is exploratory rather than a vetted literature review.

ROI and Research Gaps (~20 min) — Is PBA funding competitive with corporate campaigns given current evidence? What research would most reduce uncertainty?

rticipants share updated beliefs

operationalized PBA pivotal questions; p

live discussion of disagreements

credibility and limitations of each

Methods Debate (~30 min) — Structured exchange between demand-estimation approaches and experimental/survey approaches; credibility and limitations of each

funders

he empirical finding that most PBA purchasers are omnivores;

segments

For Anthony

What share of cultured meat companies (those with capex over $10 million) will design and build their own bioreactors by 2036?

Achievable densities in a 20kL bioreactor2420,000L used as reference scale for industrial production in most TEAs. Smaller facilities are R&D-scale. by 2036 (CM_16); binding constraints; and trade-offs in custom-built vs. off-the-shelf bioreactor design25Off-the-shelf: pharma-grade bioreactors, expensive but proven. Custom-built: fabricated to reduce capex (some claim under $1M for 20kL). The choice significantly affects capex in TEAs. Learn: bioreactor types →. Discussion space — unfold & annotate via Hypothes.is

Other Pivotal Questions Workshops 🥩 Cultivated Meat (Apr 2026) 🥗 Plant-Based Alternatives (May 2026)

We’ve reviewed this critique carefully

New to this model? Start with the Simplest Model → — a shorter version with only the biggest levers, line-of-sight explanations, and no jargon. You can carry your settings over to this Advanced Model when you’re ready.

Save / share this scenario:

Earlier versions of this model carried a separate

All costs in this model are expressed

he CDMO toll is sampled from a lognormal distribution (default p5 = $4/kg, p95 = $40/kg) representing the range of per-kg fees a future food-grade contract manufacturer might charge. See the CDMO mode section below for a full description.

VOC · CAPEX · F

Return to: Interactive Cost Model | New to this topic? How Cultured Chicken is Made | Audio Review (MP3) | Workshop (May 2026)

Sensitivity check: Users can explore partially-correlated scenarios by: - Setting maturity high but technology probabilities low (tests “scale-up succeeds, tech fails”) - Setting maturity low but one technology probability high (tests “isolated breakthrough”)

New

Cell Density / Media-Use Override Code viewof override_mode_constraints = Inputs.toggle({ label: html`Override process mode constraints <abbr style="cursor:help;text-decoration:underline dotted;font-size:0.85em;color:#888;" title="When ON: process-mode sampling is bypassed and you can specify density and media-use ranges directly. Useful for experts wanting to model specific bioreactor configurations.">(?)</abbr>`, value: urlBool("override_mode_constraints", false) }) Override process mode constraints (?)override_mode_constraints = false Code viewof density_lo = Inputs.range([10, 100], { value: urlNum("density_lo", 30), step: 10, label: "Cell Density Low (g/L)" }) viewof density_hi = Inputs.range([50, 300], { value: urlNum("density_hi", 200), step: 10, label: "Cell Density High (g/L)" }) Cell Density Low (g/L) density_lo = 30 Cell Density High (g/L) density_hi = 200 What is cell density and why does it matter so much? (click to expand) Cell density (g/L at harvest) determines how much meat you get per liter of bioreactor volume. Higher density means less media per kilogram of product, which directly reduces the largest variable cost. Density Media per kg Typical context 10 g/L ~100 L/kg Current lab scale 50 g/L ~20 L/kg Near-term commercial target 200 g/L ~5 L/kg Optimistic TEA projection This is multiplicative. If media costs $1/L, going from 10 to 50 g/L cuts media cost from $100/kg to $20/kg. Going to 200 g/L cuts it to $5/kg. Cell density is arguably the single most important technical parameter for cost reduction. Current state: Most published data shows 10-50 g/L. Some companies claim higher, but these claims are difficult to verify independently. Lever VC’s 2025 report claims 60-90 g/L has been achieved by “second generation” companies. Whether 200 g/L is achievable by 2036 is a genuine open question. What about bioreactor volume / tank size? (click to expand) Bioreactor volume is another major uncertainty that is currently implicit in this model rather than a direct parameter. The model computes total working volume as: total_volume = annual_output / (density × productivity × 365). It then applies a power-law scaling for CAPEX. But individual bioreactor tank size matters for several reasons: Factor Small tanks (2,000-5,000L) Large tanks (20,000-50,000L) Cost per liter Higher Lower (economies of scale) Contamination risk Lower Higher (single failure = large loss) Mixing/O2 transfer Easier Harder at scale Flexibility More modular Less redundancy Industry precedent Pharma standard Requires new engineering Key debate: Some companies (e.g., Vow) claim to have built 20,000L bioreactors for under $1M in 14 weeks using custom food-grade designs. If true, this dramatically changes the CAPEX picture. Humbird’s analysis assumed pharma-grade bioreactors at $50-500/L. Why it’s not a direct slider (yet): Adding individual tank size would require modeling the number of tanks, contamination batch-failure rates, and the trade-off between scale and reliability. This is a planned enhancement. For now, the Plant Capacity and Cell Density parameters together determine total working volume, and the custom reactor ratio (in full view) captures the pharma-vs-food-grade cost difference. Workshop discussion: This is one of the key cruxes for the upcoming CM workshop — what bioreactor scale is realistic, and what does it cost? Advanced: Media-use multiplier (×) What is this — and why can it be below 1? (click to expand) The model computes media volume per kg as (1000 / density) × multiplier. A value of 1 is traditional batch mode (fill reactor once, harvest); >1 is perfusion (multiple media-volume equivalents flow through during the run); <1 represents media recycling, fed-batch with concentrated feeds, or harvest-side cell concentration. The Learn page walks through all three mechanisms. Why the range changed (April 2026): the default p5–p95 was tightened from 1–10× to 0.5–3.0×. The old floor of 1.0 was too restrictive — the GFI 2023 cost-competitive scenarios assume 8–13 L/kg, which at 60–90 g/L density implies a multiplier of roughly 0.5–1.2. A floor of 1.0 mechanically excluded those scenarios no matter how high you pushed density. The new range covers both recycled/fed-batch (<1) and standard perfusion (up to ~3×); values of 5–10× remain plausible for heavily media-intensive processes but are now a stress-test region rather than the default. Show multiplier sliders Code viewof media_turnover_lo = Inputs.range([0.25, 2], { value: urlNum("media_turnover_lo", 0.5), step: 0.05, label: "Media-use multiplier p5 (low end)" }) viewof media_turnover_hi = Inputs.range([1, 10], { value: urlNum("media_turnover_hi", 3.0), step: 0.1, label: "Media-use multiplier p95 (high end)" }) Media-use multiplier p5 (low end) media_turnover_lo = 0.5 Media-use multiplier p95 (high end) media_turnover_hi = 3 Code // URL state writer: serialize every viewof value that DIFFERS FROM ITS // DEFAULT into ?key=val pairs, then debounce-write to the URL via // history.replaceState. Critical invariant: if every slider is at its // default, the URL stays bare (pathname + hash only) — no query string. // This is required so Hypothes.is can find annotations on the canonical // bare URL; a polluted URL breaks annotation lookup for every visitor. // The writer depends on every viewof name below so OJS re-runs it // whenever any input changes. Reads nothing from urlParams. { // Hard-coded defaults must stay in sync with each Inputs.range() / // Inputs.toggle() declaration above and with the reset_adoption button. const defaults = { simpleMode: true, include_blending: false, blending_share: 0.25, filler_cost: 3, include_capex: true, include_fixed_opex: true, include_downstream: false, cdmo_mode: false, cdmo_toll_p5: 4, cdmo_toll_p95: 40, bundled_media: false, bundled_media_p5: 50, bundled_media_p95: 500, plant_capacity: 20, uptime: 0.90, maturity: 0.5, target_year: 2036, p_fedbatch: 0.20, p_perfusion: 0.50, p_continuous: 0.30, override_mode_constraints: false, p_hydro: 0.75, p_recfactors: 0.5, gf_progress: 50, wacc_lo: 8, wacc_hi: 20, asset_life_lo: 8, asset_life_hi: 20, density_lo: 30, density_hi: 200, media_turnover_lo: 0.5, media_turnover_hi: 3.0 }; const state = { simpleMode, include_blending, blending_share, filler_cost, include_capex, include_fixed_opex, include_downstream, cdmo_mode, cdmo_toll_p5, cdmo_toll_p95, bundled_media, bundled_media_p5, bundled_media_p95, plant_capacity, uptime, maturity, target_year, p_fedbatch, p_perfusion, p_continuous, override_mode_constraints, p_hydro, p_recfactors, gf_progress, wacc_lo, wacc_hi, asset_life_lo, asset_life_hi, density_lo, density_hi, media_turnover_lo, media_turnover_hi }; const usp = new URLSearchParams(); let hasDiff = false; for (const [k, v] of Object.entries(state)) { const def = defaults[k]; let matches; if (typeof v === "boolean") matches = (v === def); else if (typeof v === "number") matches = Math.abs(v - def) < 1e-9; else matches = (v === def); if (!matches) { hasDiff = true; if (typeof v === "boolean") usp.set(k, v ? "1" : "0"); else if (typeof v === "number" && Number.isFinite(v)) usp.set(k, String(v)); } } if (window._urlWriteTimer) clearTimeout(window._urlWriteTimer); window._urlWriteTimer = setTimeout(() => { try { const newUrl = hasDiff ? (location.pathname + "?" + usp.toString() + location.hash) : (location.pathname + location.hash); history.replaceState(null, "", newUrl); } catch (e) { console.warn("URL state update failed:", e); } }, 300); return null; } null

In our sensitivity analysis,

doption, reactor costs, and financing. High maturity = correlated improvements.

→ Open Advanced Model with these settings

How is this cost calculated?

Full formula documentation → Model formulas & metrics Code html`<div style="margin-top:1.5rem; padding:0.8rem; background:#f0f8ff; border:1px solid #3498db; border-radius:6px; font-size:0.88em;"> <strong>Want more control?</strong> The <a href="index.html">Advanced Model</a> exposes all parameters: financing (WACC, asset life), plant capacity, cell density, media-use multiplier, CDMO mode, bundled media pricing, and more. <div style="margin-top:0.5rem;"> <a href="${(() => { const cont=Math.max(0,100-p_fedbatch_s-p_perfusion_s); const p=new URLSearchParams({target_year:target_year_s,p_hydro:(p_hydro_s/100).toFixed(2),p_recfactors:(p_recfactors_s/100).toFixed(2),p_fedbatch:(p_fedbatch_s/100).toFixed(2),p_perfusion:(p_perfusion_s/100).toFixed(2),p_continuous:(cont/100).toFixed(2),include_blending:include_blending_s?1:0,blending_share:(blending_share_s/100).toFixed(2)}); return 'index.html?'+p.toString(); })()}" style="font-weight:600;">→ Open Advanced Model with these settings</a> </div> </div>`

Always included

included

Reference commercial scale

Standard equipment life range

8–20% range Typical food/biotech financing range

Parameter Value Why fixed Industry Maturity 0.5 (neutral) At 0.5 the maturity factor has zero net effect on probabilities or financing

Probability Thresholds Code { function card(thresh, prob, label, color, bprob) { const bc = prob > 30 ? color : '#ddd'; const blend = include_blending_s && bprob !== undefined ? `<div style="font-size:0.8em; color:#1a5276; background:#f0f8ff; border-radius:3px; padding:2px 5px; margin-top:4px;"> Blended: <strong>${bprob.toFixed(1)}%</strong> chance &lt; $${thresh}/kg </div>` : ''; return `<div style="border:2px solid ${bc}; padding:0.9rem; border-radius:8px; text-align:center;"> <h5 style="margin:0 0 0.2rem;">P(Pure cells &lt; $${thresh}/kg)</h5> <h2 style="color:${color}; margin:0.2rem 0;">${prob.toFixed(1)}%</h2> <small style="color:#666;">${label}</small> ${blend} </div>`; } const grid = `<div class="grid" style="grid-template-columns:repeat(4,1fr); gap:0.75rem; margin-bottom:1.5rem;"> ${card(10, stats_s.prob_10, 'could approach conventional chicken (~$5-10/kg retail)', '#27ae60', stats_s.bprob_10)} ${card(25, stats_s.prob_25, 'range where premium cultured products may be viable', '#3498db', stats_s.bprob_25)} ${card(50, stats_s.prob_50, 'potential niche/specialty market', '#f39c12', null)} ${card(100, stats_s.prob_100, 'substantially below current lab-scale costs', '#e74c3c', null)} </div>`; const blendRow = include_blending_s ? ` <p style="font-size:0.88em; color:#1a5276; font-weight:500; margin:0.5rem 0 0.3rem;"> Blended product (${stats_s.bs*100|0}% CM + ${((1-stats_s.bs)*100)|0}% filler at $3/kg) — consumer-relevant prices: </p> <div class="grid" style="grid-template-columns:repeat(3,1fr); gap:0.6rem; margin-bottom:1.5rem;"> <div style="border:2px solid ${stats_s.bprob_5>20?'#27ae60':'#ddd'}; padding:0.8rem; border-radius:8px; text-align:center;"> <h5 style="font-size:0.85em; margin:0 0 0.2rem;">P(Blend &lt; $5/kg)</h5> <h2 style="color:#27ae60; margin:0.2rem 0;">${stats_s.bprob_5.toFixed(1)}%</h2> <small>competitive with conventional chicken</small> </div> <div style="border:2px solid ${stats_s.bprob_8>30?'#3498db':'#ddd'}; padding:0.8rem; border-radius:8px; text-align:center;"> <h5 style="font-size:0.85em; margin:0 0 0.2rem;">P(Blend &lt; $8/kg)</h5> <h2 style="color:#3498db; margin:0.2rem 0;">${stats_s.bprob_8.toFixed(1)}%</h2> <small>competitive with premium chicken/beef</small> </div> <div style="border:2px solid ${stats_s.bprob_12>50?'#f39c12':'#ddd'}; padding:0.8rem; border-radius:8px; text-align:center;"> <h5 style="font-size:0.85em; margin:0 0 0.2rem;">P(Blend &lt; $12/kg)</h5> <h2 style="color:#f39c12; margin:0.2rem 0;">${stats_s.bprob_12.toFixed(1)}%</h2> <small>affordable specialty market</small> </div> </div>` : ''; return html([grid + blendRow]); } TypeError: Cannot read properties of null (reading 'toFixed')

TypeError: Cannot read properties of null (reading 'toFixed')

Blended Product Code viewof include_blending_s = Inputs.toggle({ label: "Show blended product analysis", value: urlBool_s("include_blending", false) })

all parameters

Projected 2036 Cost Distribution:where(.plot-d6a7b5) { --plot-background: white; display: block; height: auto; height: intrinsic; max-width: 100%; } :where(.plot-d6a7b5 text), :where(.plot-d6a7b5 tspan) { white-space: pre; }

A first look at cultured chicken cost — for exploration and understanding

Year

Continuous (auto): 35%

xposes only the biggest levers on cultured chicken

WACC

Background

Parameters

Mix

·

For more complete analysis with all parameters exposed

38.7% chance blended product (25% CM, $3/kg filler) < $10/kg

Parameter Sensitivity: Dollar Swing in Mean Unit Cost :where(.plot-d6a7b5) { --plot-background: white; display: block; height: auto; height: intrinsic; max-width: 100%; } :where(.plot-d6a7b5 text), :where(.plot-d6a7b5 tspan) { white-space: pre; }

Asset Life High

Simplified view: Less pivotal parameters (plant capacity, uptime, financing costs, media-use multiplier) are set to reasonable defaults. In our sensitivity analysis, these contribute less than 10% of the variance in cost estimates. Switch off to adjust all parameters. Code // Reactive style block to hide/show full-mode-only and cdmo-only inputs html`<style> .full-mode-only { display: ${simpleMode ? 'none' : 'block'}; } .cdmo-only { display: ${cdmo_mode ? 'block' : 'none'}; } .override-mode-only { display: ${override_mode_constraints ? 'block' : 'none'}; } .separable-only { display: ${bundled_media ? 'none' : 'block'}; } .bundled-only { display: ${bundled_media ? 'block' : 'none'}; } .blending-only { display: ${include_blending ? 'block' : 'none'}; } </style>` .full-mode-only { display: none; } .cdmo-only { display: none; } .override-mode-only { display: none; } .separable-only { display: block; } .bundled-only { display: none; } .blending-only { display: none; }

Cultured Chicken Production Cost Model CodeShow All CodeHide All CodeView Source

P(Scalable Growth Factor (GF)

Cell Density (g/L)

Which parameters have the most impact on the final cost? Each bar shows the dollar swing in mean unit cost between simulations where the parameter is in its top 10% versus its bottom 10%. Larger bars = bigger levers on cost. Code { const uc = results.unit_cost; // Deduplicated parameter list. // Removed vs. previous version (see explainer below for details): // • L/kg (volume) — deterministic function of density × media-use multiplier // • Uses Hydrolysates — regime-switch subsumed into Media $/L // • Has Cheap GFs — regime-switch subsumed into GF Price / GF Quantity const params = [ {name: "Cell Density (g/L)", data: results.density_samples, kind: "primitive"}, {name: "Media-use multiplier (×)", data: results.media_turnover_samples, kind: "primitive"}, {name: "Media $/L (incl. hydrolysate regime)", data: results.media_cost_L_samples, kind: "mixture"}, {name: "GF Price ($/g, incl. regime)", data: results.price_recf_samples, kind: "mixture"}, {name: "GF Quantity (g/kg, incl. regime)", data: results.g_recf_samples, kind: "mixture"}, {name: "Industry Maturity (latent — see note)", data: results.maturity_samples, kind: "latent"}, {name: "Plant Capacity (kTA)", data: results.plant_kta_samples, kind: "primitive"}, {name: "Utilization Rate", data: results.uptime_samples, kind: "primitive"} ]; const swings = params.map(p => ({ name: p.name, kind: p.kind, swing: conditionalSwing(p.data, uc, 0.10) })); const sorted = swings .map(s => ({...s, absSwing: Math.abs(s.swing)})) .sort((a, b) => b.absSwing - a.absSwing); const maxAbs = Math.max(...sorted.map(s => s.absSwing), 1); const pad = maxAbs * 0.30; const tornadoPlot = Plot.plot({ width: 900, height: 440, marginLeft: 290, marginRight: 100, x: { label: "Δ mean unit cost ($/kg): top 10% − bottom 10% of parameter", domain: [-maxAbs - pad, maxAbs + pad], grid: true, labelOffset: 40, tickFormat: d => (d >= 0 ? "+$" : "−$") + Math.abs(d).toFixed(0) }, y: { label: null, tickFormat: d => d, tickSize: 0 }, color: { domain: ["Increases cost", "Decreases cost"], range: ["#e74c3c", "#27ae60"] }, style: { fontSize: "13px" }, marks: [ Plot.barX(sorted, { y: "name", x: "swing", fill: d => d.swing > 0 ? "Increases cost" : "Decreases cost", sort: {y: "-x", reduce: d => Math.abs(d)} }), Plot.ruleX([0], {stroke: "black", strokeWidth: 1}), Plot.text(sorted, { y: "name", x: d => d.swing > 0 ? d.swing + maxAbs * 0.025 : d.swing - maxAbs * 0.025, text: d => (d.swing > 0 ? "+$" : "−$") + Math.abs(d.swing).toFixed(1) + "/kg", textAnchor: d => d.swing > 0 ? "start" : "end", fontSize: 12, fontWeight: 500 }) ] }); return html`<div style="font-size: 1em;"> <div style="font-weight: normal; font-size: 1.05em; margin-bottom: 0.5rem; color: #333;">Parameter Sensitivity: Dollar Swing in Mean Unit Cost</div> ${tornadoPlot} </div>`; }

Sensitivity Analysis (Tornado Chart)

Technology Adoption & Process Mode (Realized) Code { const isOverride = results.mode_is_override; const pct_fb = (results.pct_fedbatch * 100).toFixed(0); const pct_pf = (results.pct_perfusion * 100).toFixed(0); const pct_ct = (results.pct_continuous * 100).toFixed(0); const modeCard = isOverride ? html`<div class="card" style="border: 1px solid #ddd; padding: 1rem; border-radius: 8px; text-align: center; grid-column: 1 / -1;"> <h5>Process Mode</h5> <div style="color: #888; font-size: 0.9em;">Override — using manual density / media-use ranges</div> </div>` : html`<div class="card" style="border: 1px solid #16a085; padding: 1rem; border-radius: 8px; grid-column: 1 / -1;"> <h5 style="margin-bottom:0.5rem;">Process Mode (realized)</h5> <div style="display:flex; gap:1.5rem; justify-content:center; font-size:1.1em;"> <span>Fed-batch <strong style="color:#e67e22;">${pct_fb}%</strong></span> <span>Perfusion <strong style="color:#16a085;">${pct_pf}%</strong></span> <span>Continuous <strong style="color:#2980b9;">${pct_ct}%</strong></span> </div> </div>`; return html`<div class="grid" style="grid-template-columns: repeat(2, 1fr); gap: 1rem; margin: 2rem 0;"> <div class="card" style="border: 1px solid #ddd; padding: 1rem; border-radius: 8px; text-align: center;"> <h5>Hydrolysates Adopted</h5> <h2 style="color: #27ae60;">${(results.pct_hydro * 100).toFixed(0)}%</h2> <small>of simulations use hydrolysates</small> </div> <div class="card" style="border: 1px solid #ddd; padding: 1rem; border-radius: 8px; text-align: center;"> <h5>Cheap Growth Factors</h5> <h2 style="color: #9b59b6;">${(results.pct_recf_cheap * 100).toFixed(0)}%</h2> <small>of simulations have cheap factors</small> </div> ${modeCard} </div>`; }

average contribution

Component Distributions

Where does the cost come from? This chart shows the average contribution of each cost component across all simulations. The largest bars are the cost drivers to focus on — these are where technological progress or parameter uncertainty has the most impact. Code { const mediaLabel = bundled_media ? "Complete Media (incl. GFs)" : "Media (incl. basal micros)"; const allComponents = [ {name: mediaLabel, value: mean(results.cost_media), color: "#27ae60"}, {name: "Growth Factors", value: mean(results.cost_recf), color: "#9b59b6"}, {name: "Other VOC", value: mean(results.cost_other_var), color: "#7f8c8d"}, {name: "CAPEX (annualized)", value: mean(results.cost_capex), color: "#e74c3c"}, {name: "Plant overhead OPEX", value: mean(results.cost_fixed), color: "#f39c12"}, {name: "CDMO Toll", value: mean(results.cost_cdmo_toll), color: "#e67e22"}, {name: "Downstream", value: mean(results.cost_downstream), color: "#1abc9c"} ]; // Filter out zero-value components (e.g., downstream when not included) const components = allComponents.filter(c => c.value > 0.001).sort((a, b) => b.value - a.value); const total = components.reduce((s, c) => s + c.value, 0); const chartContainer = document.createElement("div"); chartContainer.style.position = "relative"; // Expand/collapse button const expandBtn = document.createElement("button"); expandBtn.textContent = "Expand Chart"; expandBtn.style.cssText = "padding: 0.3rem 0.7rem; font-size: 0.8rem; cursor: pointer; border: 1px solid #ccc; border-radius: 4px; background: #f8f9fa; margin-bottom: 0.5rem;"; let expanded = false; expandBtn.onclick = () => { expanded = !expanded; expandBtn.textContent = expanded ? "Collapse Chart" : "Expand Chart"; chartEl.replaceWith(makeChart(expanded)); chartEl = chartContainer.querySelector(".cost-breakdown-plot"); }; chartContainer.appendChild(expandBtn); function makeChart(large) { const w = large ? 1200 : 1000; const h = large ? 700 : 580; const fontSize = large ? 14 : 13; const p = Plot.plot({ width: w, height: h, marginLeft: 200, marginRight: 140, x: { label: "Average Cost ($/kg)", grid: true }, y: { label: null }, marks: [ Plot.barX(components, { y: "name", x: "value", fill: "color", sort: {y: "-x"} }), Plot.text(components, { y: "name", x: d => d.value + 0.5, text: d => `$${d.value.toFixed(2)} (${(d.value/total*100).toFixed(0)}%)`, textAnchor: "start", fontSize: fontSize }) ], title: `Cost Breakdown by Component (Total: $${Math.round(total)}/kg)` }); p.classList.add("cost-breakdown-plot"); return p; } let chartEl = makeChart(false); chartContainer.appendChild(chartEl); return chartContainer; } Expand Chart

Show blended product cost

Why it matters: If production costs for pure cells reach ~$10/kg, even 100% cultured products could compete with conventional chicken. At $25-50/kg, hybrid products with moderate cell inclusion rates may still reach price parity. If costs remain >$100/kg, even hybrid products face significant price premiums. These thresholds inform whether animal welfare interventions should prioritize supporting this industry. Code html`<div class="grid" style="grid-template-columns: repeat(3, 1fr); gap: 1rem; margin-bottom: 2rem;"> <div class="card" style="background: linear-gradient(135deg, #3498db, #2980b9); color: white; padding: 1.5rem; border-radius: 8px;"> <h4 style="margin: 0; opacity: 0.9;">Median Pure Cell Mass Cost (p50)</h4> <h2 style="margin: 0.5rem 0;">$${Math.round(stats.p50)}/kg</h2> <small>$/kg pure cell mass (wet weight) — half of simulations above, half below</small> </div> <div class="card" style="background: linear-gradient(135deg, #27ae60, #1e8449); color: white; padding: 1.5rem; border-radius: 8px;"> <h4 style="margin: 0; opacity: 0.9;">Optimistic (p5)</h4> <h2 style="margin: 0.5rem 0;">$${Math.round(stats.p5)}/kg</h2> <small>Only 5% of simulations cheaper</small> </div> <div class="card" style="background: linear-gradient(135deg, #e74c3c, #c0392b); color: white; padding: 1.5rem; border-radius: 8px;"> <h4 style="margin: 0; opacity: 0.9;">Pessimistic (p95)</h4> <h2 style="margin: 0.5rem 0;">$${Math.round(stats.p95)}/kg</h2> <small>95% of simulations cheaper</small> </div> </div> ${include_blending ? html`<div style="background: #eaf7ea; border-left: 4px solid #27ae60; padding: 0.8rem 1rem; margin-top: 0.5rem; font-size: 0.9em;"> <strong>Blended product estimate (${Math.round(blending_share * 100)}% CM, ${Math.round((1-blending_share)*100)}% filler at $${filler_cost}/kg):</strong> Median <strong>$${stats.blended_p50.toFixed(1)}/kg</strong> · 90% CI: $${stats.blended_p5.toFixed(1)} – $${stats.blended_p95.toFixed(1)}/kg </div>` : html`<div style="background: #fef9e7; border-left: 4px solid #f39c12; padding: 0.8rem 1rem; margin-top: 0.5rem; font-size: 0.9em;"> <strong>Hybrid product estimate:</strong> At a CM inclusion rate of ~25% with plant-based filler at ~$3/kg, the blended ingredient cost would be approximately <strong>$${(stats.p50 * 0.25 + 3 * 0.75).toFixed(1)}/kg</strong> (median). Enable "Show blended product cost" in the sidebar to adjust these assumptions. </div>`} </div>`

Results Summary Code html`<div style="background: #f8f9fa; padding: 1rem 1.25rem; border-left: 4px solid #3498db; margin-bottom: 1.5rem; font-size: 0.95em; line-height: 1.6;"> <strong>What these numbers represent:</strong> Simulated <strong>production cost per kilogram of pure cultured chicken cells</strong> (<span title="Wet weight = the mass of cells as harvested from the bioreactor, including water content (~70-80%). This is the standard output basis used in most TEAs (Humbird 2021, Pasitka 2024). It does NOT include downstream processing into structured products, blending with plant-based ingredients, or retail margins. For comparison: Humbird reports $37/kg wet cell mass; Pasitka reports $13.75/kg wet cell mass (large perfusion). The widely-cited ~$6/lb Pasitka figure is for a 50/50 hybrid product, not pure cell mass. See our TEA Comparison page for details." style="text-decoration: underline dotted; cursor: help;">wet weight, unprocessed &#9432;</span>) in <strong>${target_year}</strong>, based on ${stats.n.toLocaleString()} Monte Carlo simulations. This is the cost to produce cell mass in a bioreactor — not the cost of a consumer product, and not retail price. <a href="compare.html" style="font-size: 0.9em;">[Compare to published TEAs →]</a> <br><br> <strong><span title="UPSIDE Foods' chicken cutlet is a blend of cultured chicken cells and plant-based ingredients. SuperMeat's chicken burger used ~30% cultured cells. The GFI State of the Industry 2024 report notes that 'hybrid products combining cultivated and plant-based ingredients are the most likely near-term path to market.' Eat Just/GOOD Meat's Singapore-approved product uses cultured chicken in a plant-protein matrix.">Pure cells vs. consumer products:</span></strong> Most cultivated meat products on the market or in development are <em>hybrid products</em> — blending a fraction of cultured cells with plant-based or mycoprotein ingredients. A product with (say) 20% cultured cells and 80% plant-based filler at $3/kg would have a blended ingredient cost far below the pure-cell cost shown here. The "price parity with conventional meat" threshold may therefore be achievable at higher per-kg cell costs than these numbers suggest. <br><br> <strong>Why it matters:</strong> If production costs for pure cells reach <strong>~$10/kg</strong>, even 100% cultured products could compete with conventional chicken. At <strong>$25-50/kg</strong>, hybrid products with moderate cell inclusion rates may still reach price parity. If costs remain <strong>>$100/kg</strong>, even hybrid products face significant price premiums. These thresholds inform whether animal welfare interventions should prioritize supporting this industry. </div>`

Component Distributions

Process Mode Mix Code viewof p_fedbatch = Inputs.range([0, 1], { value: urlNum("p_fedbatch", 0.20), step: 0.05, label: html`Fed-batch weight <abbr style="cursor:help;text-decoration:underline dotted;font-size:0.85em;color:#888;" title="Low density (5–30 g/L), moderate media use (1–2×). Semi-continuous: nutrient-concentrated feeds added periodically. Less efficient than perfusion.">(?)</abbr>` }) viewof p_perfusion = Inputs.range([0, 1], { value: urlNum("p_perfusion", 0.50), step: 0.05, label: html`Perfusion weight <abbr style="cursor:help;text-decoration:underline dotted;font-size:0.85em;color:#888;" title="Medium-high density (30–150 g/L), higher media throughput (1–5×). Continuous media exchange with cell retention. Currently the industry standard for high-density CM production.">(?)</abbr>` }) viewof p_continuous = Inputs.range([0, 1], { value: urlNum("p_continuous", 0.30), step: 0.05, label: html`Continuous weight <abbr style="cursor:help;text-decoration:underline dotted;font-size:0.85em;color:#888;" title="Highest density (50–200 g/L), efficient media use (0.5–3×). Near-steady-state operation; cells grown and harvested continuously with optimized recycling.">(?)</abbr>` })

Fed-batch weight

Process Mode Mix Code viewof p_fedbatch = Inputs.range([0, 1], { value: urlNum("p_fedbatch", 0.20), step: 0.05, label: html`Fed-batch weight <abbr style="cursor:help;text-decoration:underline dotted;font-size:0.85em;color:#888;" title="Low density (5–30 g/L), moderate media use (1–2×). Semi-continuous: nutrient-concentrated feeds added periodically. Less efficient than perfusion.">(?)</abbr>` }) viewof p_perfusion = Inputs.range([0, 1], { value: urlNum("p_perfusion", 0.50), step: 0.05, label: html`Perfusion weight <abbr style="cursor:help;text-decoration:underline dotted;font-size:0.85em;color:#888;" title="Medium-high density (30–150 g/L), higher media throughput (1–5×). Continuous media exchange with cell retention. Currently the industry standard for high-density CM production.">(?)</abbr>` }) viewof p_continuous = Inputs.range([0, 1], { value: urlNum("p_continuous", 0.30), step: 0.05, label: html`Continuous weight <abbr style="cursor:help;text-decoration:underline dotted;font-size:0.85em;color:#888;" title="Highest density (50–200 g/L), efficient media use (0.5–3×). Near-steady-state operation; cells grown and harvested continuously with optimized recycling.">(?)</abbr>` })

The beliefs form is at uj-cm-workshop.netlify.app/beliefs.html. A short pre-workshop version goes out ~May 1. The in-workshop portion is lightweight: we'll orient you to the form at the breaks, and ask for a live response on CM_01 (the focal cost question) and one or two subquestions.

What should The Unjournal evaluate next — which papers, which questions, which specialists? [Suggested discussants]Suggested discussants:Matt McNulty (Tufts CCA) — wants to discuss tea_review + business_environment; strategic view of research priorities from an academic CM centerDavid Manheim (Technion/ALTER) — evaluator; wants to discuss modeling_hack; systematic view on what would be most informative to evaluate

CM_01

Believer Meats published favorable TEA results in Nature Food yet shut down in late 2025.

process choice

EU/CET participants likely available (15:00–16:00 CET):Aleksandra Fuchs (ACIB, Graz) — presenting in S1; likely joining earlyJordi Morales-Dalmau (Cultimate Foods, Berlin) — CET; indicated flexible availabilityMirjam Capuder (Univ Maribor) — indicated 15:00 CET availability on May 8Tom Bry-Chevalier (Univ de Lorraine) — CET; indicated May 8 availableJakub Kozlowski (Zurich

(ideally presented by a participant with direct CM expertise; otherwise DR will walk through the key model inputs and assumptions).

If you are presenting slides in S2 and would like them shared more widely, just let us know and we will circulate them separately.

batch ≈ 1,

Batch vs. Perfusion: Two O

Batch v

Against this, Kalkar argued the opposite — that the contested empirical picture creates an opening: it may make sense to be a first-mover and evaluate now, given Ord's reframed 2027+ predictions"it may actually make sense to be a 'first-mover' and evaluate it now. Given that Toby Ord's reframed his predictions to 2027+, there's a window of opportunity"— Uma Kalkar.

I think engage with funders of AI and catastrophic risk, alignment etc. (I think Schmidt is interested, but are only funding research right now) -- it might be useful to reach out to them to provide an open evaluation of the work that they are commissioning / providing grants to. This way we also get to engage with AI researchers working at the forefront (at least in economics, broadly).

Hypothesis — click the < tab on the right edge of any dashboard page

Fixed

basal media

Assessor prioritization rating

The NBER format implies a working-paper stage

▾ Details