In this sense, AbstractExplorer enables dialectical activities that users may otherwise have found to be too tedious or difficult to engage with.

Like prior Structural Mapping Theory (SMT)-informed work in text corpora representation, AbstractExplorer's features have enabled some users to see more of both the overview and the details at the same time, facilitating abstraction without losing context.

We posit that our approach can generalize to other domains such as journalism, code synthesis, and social media analytics where visual alignment of text can enable meaningful comparisons of underlying patterns to identify relational clarity.

In this work, we introduce a new paradigm for exploring a large corpus of small documents by identifying roles at the phrasal and sentence levels, then slice on, reify, group, and/or align the text itself on those roles, with sentences left intact.

Dialectical activities cannot be done on a user's behalf by AI; with variation affordances, AI is supporting the user's engagement with the data themselves.

We demonstrate how slicing sentences according to roles and visually aligning them can help readers perceive cross-document relationships in a coherent manner.

Our work demonstrates that designs informed by Structure-Mapping Theory can support users in navigating, making use of, and engaging with variation present in information.

pre-computing and reifying cross-document analogous relationships make it psychologically possible for users to engage—if they are willing to be guided by it. (Lower NFC users are more likely to fall into this category.)

Participants with lower NFC more frequently preferred and experienced less cognitive load when skimming with all the features enabled.

These results suggest that these three features lose their effectiveness when not used together.

Lower NFC participants were generally guided by emergent visual patterns created by the interactions between features, especially blocks of color spanning multiple sentences created when all three features are turned on.

Claude can even automatically learn from _other_ Slack channels and data sources, if it's granted permission.

Benchmarked: **P@5 49.1%, R@5 97.9%** on a 240-page Opus-generated rich-prose corpus, **+31.4 points P@5** over its graph-disabled variant and over ripgrep-BM25 + vector-only RAG by a similar margin.

The point of building a 100K-page brain is to use it as a strategic moat. To never lose context. To query what's in your own head without re-reading it. The brain layer is what makes the moat usable. The 24/7 dream cycle is what keeps it sharp.

Every page write extracts entity refs and creates typed edges (attended, works_at, invested_in, founded, advises) with zero LLM calls.

A synthesis layer that gives you the actual answer. Synthesized, well-cited prose across people, companies, deals, and ideas. Not 'here are 10 chunks that mention your query'; an actual answer with citations and an explicit note on what the brain doesn't know yet. The gap analysis is the part that changes how you use the brain.

Knowledge workers primarily use Codex to create reports, spreadsheets, presentations, contracts, and other work products.

The fastest-growing knowledge-worker tasks are data analysis, research, and knowledge artifact creation.

A Treatise concerning thePrinciples of I-Iuman Kno,vledge

Relational cate-gories are categories for which the basis for membership is participation in a commonrelational structure; thus, they differ from the more studied entity categories, such as tulipand spoon, whose members share many intrinsic properties.

Our main focus is on relational abstractions, includingprinciples, rules, and schemas, as well as abstract relational categories.

For example, causal system is more abstract than posi-tive feedback system, which in turn is more abstract than the specific positive feedbacksystem by which the melting of polar ice causes lower reflectance of the sun’s heat, lead-ing in turn to more rapid melting.

Wetake the process of abstraction to be one of decreasing the specificity (and therebyincreasing the scope) of a concept.

Many such abstractions are expressed as rela-tional categories—categories like evidence, counterfactual, and proportion, and on a moremundane level, bargain, ally, and rescue.

Theassertions that make up abstract knowledge are variously referred to as schemas, rules,abstractions, principles, or overhypotheses

Abstract structured knowledge is a key feature of higher order cognition (Gentner &Medina, 1998; Hummel, 2011; Markman, 1999; Tenenbaum & Griffiths, 2001).

export controls are leakier than previously understood

Ultimately, by inducing a highly aligned cross-embodiment representation, UniT offers a scalable path to distill vast human knowledge into general-purpose humanoid capabilities.

All of knowledge work has this problem. It's hard to objectively judge the quality of someone's work without spending a lot of effort on it. Therefore everyone relies heavily on proxy measures.

🔹 **Rich World Knowledge:** Leads all current open models, trailing only Gemini-3.1-Pro.

The LLM took an entirely different route, using a formula that was well known in related parts of math, but which no one had thought to apply to this type of question.

A nuclear weapons program lost the ability to make a material it invented. The knowledge existed only in people, and the people were gone.

By accessing the frequently updated knowledge base, agents can ground their responses in the most recent information from Android developer docs, Firebase, Google Developers, and Kotlin docs. This ensures that even if an LLM's training cutoff is a year old, it can still provide guidance on the latest frameworks and patterns we recommend today.

Claude 4.6 had a section specifically clarifying that 'Donald Trump is the current president of the United States and was inaugurated on January 20, 2025'

LLMs take knowledge from millions of people who have written web content or posted in places like Reddit and Wikipedia, interacted with chatbots, and generated other types of data, and make that available to individuals on demand.

In this way the context layer can become a multi-dimensional corpus where code lives alongside natural language, capturing any context an agent might need.

The crux of the problem at hand is that the agent isn't given the proper business context to answer even the most basic questions. This is representative of a larger gap that's present in building automated AI systems within organizations – there needs to be up-to-date and maintained context that not only understands how an enterprise works and how the data systems are structured, but also maintains the tribal knowledge to tie everything together.

Scientists must work across large volumes of literature, specialized databases, experimental data, and evolving hypotheses in order to generate and evaluate new ideas.

A surprising number of people are now employed as model trainers, feeding their human expertise to automated systems.

The knowledge was always there. The model withheld it based on who was asking.

Performance on knowledge-heavy tasks depends strongly on model size and training, while reasoning-oriented models show clear gains on tasks requiring logic, learning, abstraction, and social inference.

We evaluate 452 tasks from the public APEX-Agents dataset spanning investment banking, management consulting, and corporate law

The human's job is to curate sources, direct the analysis, ask good questions, and think about what it all means. The LLM's job is everything else.

Think of fan wikis like Tolkien Gateway — thousands of interlinked pages covering characters, places, events, languages, built by a community of volunteers over years. You could build something like that personally as you read, with the LLM doing all the cross-referencing and maintenance.

Humans abandon wikis because the maintenance burden grows faster than the value. LLMs don't get bored, don't forget to update a cross-reference, and can touch 15 files in one pass. The wiki stays maintained because the cost of maintenance is near zero.

good answers can be filed back into the wiki as new pages. A comparison you asked for, an analysis, a connection you discovered — these are valuable and shouldn't disappear into chat history.

Obsidian is the IDE; the LLM is the programmer; the wiki is the codebase.

the wiki is a persistent, compounding artifact. The cross-references are already there. The contradictions have already been flagged. The synthesis already reflects everything you've read.

Instead of just retrieving from raw documents at query time, the LLM incrementally builds and maintains a persistent wiki — a structured, interlinked collection of markdown files that sits between you and the raw sources.

the maintenance burden grows faster than the value.

good answers can be filed back into the wiki as new pages.

the wiki is a persistent, compounding artifact.

the LLM is rediscovering knowledge from scratch on every question.

Worse, they learn nothing from past work. Institutional knowledge lives in textbooks and the minds of a few experts. None of it is captured in the tools themselves.

These could lead to especially large and fast spillovers if there are "four minute mile" effects — after one AI lab makes a breakthrough, other labs realise they can do it too, so they pour effort into reimplementation.

We find internal representations of emotion concepts, which encode the broad concept of a particular emotion and generalize across contexts and behaviors it might be linked to.

it is impossible for developers to specify how the Assistant should behave in every possible scenario. In order to play the role effectively, LLMs draw on the knowledge they acquired during pretraining, including their understanding of human behavior

Cai et al. [117] interviewed 21 pathologists who used a deep neural network to aid in thediagnosis of prostate cancer. The interviews showed that pathologists needed to learn moreabout the network’s strengths and limitations to use it effectively. They also wanted to knowthe design objective of the network and the kind of data on which it was trained.

The inclusion of counterfactuals often resulted in a substantial increase in precision, indicating that the models were better able to correctly classify relevant instances while reducing false positives.

By visualizing these consistent pattern rules, users may be better understanding the behavior of the model through inference projection.

Mocha addresses two seemingly contradictory objectives: (1) generating labeled data that diversifies the training dataset to aid the model's learning, and (2) maintaining structural consistency across the batches of data presented to users to support their cognitive processes.

The results of our study indicate that participants spent significantly less time annotating batches of counterfactuals when they were rendered according to SAT compared to other conditions i.e., supporting the participants' selective focus on the varying phrases, rather than phrases that stay consistent.

Mocha exemplified the application of human cognition and concept learning theories in the interactive machine learning pipeline to support the negotiation of conceptual boundaries for bi-directional human-AI alignment.

This pattern of selective attention suggests that the visual cues provided by Mocha effectively guided participants to focus on more relevant information within the context of unchanged text when making their labeling decisions.

Overall, the incorporation of counterfactuals has generally improved the models' F1 scores, driven largely by the improvements in precision. This suggests that counterfactuals have effectively improved performance without necessitating a significant trade-off between precision and recall.

The inclusion of counterfactuals often resulted in a substantial increase in precision, indicating that the models were better able to correctly classify relevant instances while reducing false positives. This improvement suggests that the counterfactuals provided essential information that helped refine the models' decision boundaries.

By visualizing these consistent pattern rules, users may be better understanding the behavior of the model through inference projection [26]. This can not only boosts the model's performance but also enable participants to validate or correct the model during the interactive training process.

Thus, the integration of both theories enables users to efficiently process and compare variations, leading to more informed decisions and a clearer understanding of the model's behavior.

By helping users see alignable differences, SAT-based rendering helps users focus on key variations that are essential to changing the data item's label, making it easier to interpret the effects of changes and their significance.

We argue that these two theories form a symbiotic relationship (Fig. 6). Variation Theory provides the conceptual basis for generating structurally consistent differences, while Structural Alignment Theory (SAT) enhances the user's ability in recognizing and processing these differences.

Participants were able to efficiently focus on key differences between the original and counterfactual examples, which facilitated more efficient annotations.

The results from our user study suggest that both the participants and the model benefited from the Variation Theory (VT)-based counterfactuals and Structural Alignment Theory (SAT)-based rendering.

Thus, the integration of both the-ories enables users to efficiently process and compare variations,leading to more informed decisions and a clearer understanding ofthe model’s behavior.

This gap between feeling and explanation isn’t mysterious. Neuroscientist Antonio Damasio spent decades documenting what he calls “somatic markers”—physiological responses that guide decision-making before conscious reasoning engages. These signals aren’t primitive or irrational. They’re compressed expertise—the accumulated wisdom of lived experience, speaking faster than language.

I had written that the only thing that all the different cultures hadin common was the human brain. Indigenous cultures, separated byvast spans of space and time, did not teach their incredibly similarmemory tools to one another. They must all use the same methodsfor innately human reasons.

The answer lies in our ability to use story, art and music toenhance our learning skills, and in our intense connection to thedetails within our physical environment. We can use these skills notonly to store information and increase our knowledge but also, evenmore importantly, to explore new ideas. They help us see creativesolutions to the way things are, and envisage the way they could be.

All Indigenous cultures rely on music as a primaryknowledge technology.

David KanoshYooḵis’kooḵéikTlingit Elder and storyteller,South-Eastern Alaska

I made my own memory boards. As I glued each piece to theplywood, I thought about the stories I would tell with this memoryboard. I had spent an afternoon just putting on five pebbles! Howdid my grandmother make it look so easy? Practice. She’d been atthis for a while.

The passing down of knowledge isn’t just a transfer — it’s a kind of love.

Münster, January 13, 1968

Gettier claims to have shown that the JTB account is inadequate because it does not account for all of the necessary and sufficient conditions for knowledge.

My goal in this is to treat knowledge as a living, collaborative process, not a series of finished articles.

I would like to give the Zettelkästen a much larger online presence than they currently have, opening up new resonances between the digital gaze, the materiality of the archive, and the affordances of both.

we all come to our expressions from our own perspective our own uh Origins our own traumas our own you know Fillin the blank um when we share them with others we have to make ourselves understood

some of us should venture toembark on a synthesis of facts and theories, albeit withsecond-hand and incomplete knowledge of some of them -and at the risk of making fools of ourselves.

W e have inherited from our forefathers the keen longing forunified, all-embracing knowledge.

if I can really let go of any theory of who I am, then I'll let go of any fear.

t keeps you from just talking abstractly about this stuff and and and and being real about it is what do I really feel about it?

Sabin asked the boy in disbelief, “Why not?”Silence—then finally he sheepishly replied, “Well, I thought if I didn’t unrollmy sleeping bag, I wouldn’t have to roll it up again.”This young boy painfully froze for hours because he didn’t want to endure thetorturous five minutes of rolling up his sleeping bag

The shaded afterlife of Leonardo’s notebooks – ‘without parallel in theintellectual history of word and image’, as Kemp describes them – stands insharp relief when we light it with the long, powerful burn of Pacioli’sposthumous – albeit near-anonymous – career. There’s no doubt thatLeonardo was the greater thinker, but his creative achievements madescarcely any impression compared to the impact of the universal adoptionof his friend’s Summa.

commodification of knowledge

K-mer-based tools such as Kraken2 [14], KrakenUniq [15], Bracken [16], Centrifuge [17], CLARK/CLARKS [18, 19], Ganon [20, 21], Taxor [22], and Sylph [23] are known for their speed and scalability to large databases, but often trade precision for speed

I recommend focusing on acquiring both common capital at the local level, and to scale up cooperation at the level of planetarity.

it is quite possible that our empirical knowledge is a compound of that which we receive through impressions, and that which the faculty of cognition supplies from itself (sensuous impressions giving merely the occasion), an addition which we cannot distinguish from the original element given by sense, till long practice has made us attentive to, and skilful in separating it.

For our purposes, we will define a habit as the intersection of knowledge, skill, anddesire. Knowledge is the theoretical paradigm, the what to do and the why. Skill is thehow to do. And desire is the motivation, the want to do. In order to make something ahabit in our lives, we have to have all three

each worldview brings forth new insights, solutions, and qualities, as well as more problematic aspects.

In 2023, the reported global installed capacity of FPVwas 2.55 GWp and practical potential electricity generationfrom FPV might be able to produce up to 9434 TWh year−1with 30% array coverage on >100,000 reservoirs.

The ponds used in this study havealmost no watershed and are not connected to a stream orriver, thus limiting inputs of organic matter and nutrients fromoutside the ecosystem

Sustainability trade-offs of FPV in aquatic ecosystems aredriven by a range of interactive factors, and the goal of net-zeroemissions is affected by biogeochemical processes on land andin water.

Shifts in primaryproducer abundance and dominance following FPV installationmight influence GHG cycling in several ways, including byaltering rates of photosynthesis and CO2 uptake,51,52 loading oforganic matter to the sediment,29 and mixing and stratificationdynamics.

Changes in sediment and watercolumn respiration also will be reflected in water column GHGdynamics (e.g., increased concentrations of CO2 and CH4following panel installation) and air−water GHG ex-change.

The sum of GHG effectsassociated with FPV can be represented by air−water GHGexchange that integrates GHG dynamics taking place withinthe waterbody

GHG emissions associated with water-use change duringand after FPV installation require different considerations andaccounting than for terrestrial PV

following FPV deployment, ponds with FPV becamecolder than ponds without and tended to have more uniformtemperatures throughout the water column (Figure S1).

Experimental Ponds facility to estimate an annual pondemission enrichment of 151.3 g of CO2-eq m−2 year−1 forponds with FPV.

We can combine the average difference in GHG emissions inponds with and without FPV determined here with apreviously published annual GHG budget for the Cornell

Ebullitive CH4 emissions were onaverage nearly twice as high in ponds with FPV (0.21 ± 0.04mmol CH4 m−2 h−1) compared to ponds without FPV (0.11 ±0.02 mmol CH4 m−2 h−1) following FPV installation (p =0.031; Figure 5).

Gas transfer velocities (i.e., k600values) in the FPV-covered center of ponds were 4 times lowerfor CO2 (1.27 ± 0.18 cm h−1) and 3 times lower for CH4 (1.42± 0.59 cm h−1) than open pond centers in ponds without FPV(5.42 ± 2.23 and 4.25 ± 1.22 cm h−1 for CO2 and CH4respectively; Table S3), though these differences were notstatistically significant (p = 0.157 for k600CO2 and p = 0.107 fork600CH4), most likely due to the relatively small sample size.

In both treatments, CH4 dynamics followed seasonalpatterns, with the highest concentrations occurring duringwarm summer months

Ebullitive CH4 fluxesfollowed typical seasonal patterns in ponds both with andwithout FPV

wind speed was determinedby correcting wind speed data from the Ithaca-Tompkinsweather station (NOAA Station WBAN-94761), which islocated ∼2 km from the experimental ponds, using a previouslyestablished correction factor.

We sampled 16 ponds at theCornell Experimental Ponds Facility in Ithaca, NY, USA, insummer 2022 to identify six ponds that were most similarbased on the plant community, temperature, dissolved oxygen,pH, conductivity, dissolved nutrients, and dissolved GHGconcentration

We deployed FPV arrays on constructed ponds at the CornellExperimental Pond Facility in New York, USA in summer2023 (Figure 1).

CH4 ebullition from small waterbodies is also controlled byfactors such as temperature, dissolved oxygen, and organicmatter availability that are likely affected by FPV installa-tion.

Production and consumption of CO2 and CH4 in ponds,lakes, and reservoirs are dependent on dissolved oxygen,temperature, and the balance between primary production andrespiration

Energy production technologies require land and can alterlandscape GHG emissions,16−20 which may be particularlyimportant when considering the carbon cost of renewableenergy production from technologies touted as low carbon.

FPVdeployment may alter greenhouse gas (GHG) production and emissions fromwaterbodies by changing physical, chemical, and biological processes, which canhave implications for the carbon cost of energy production with FPV.

Demir and Taşkın (2013) conducted the LCA of a con-ceptual wind farm at a designated area in Turkey with highwind potential.

the results of this study were compared withsimilar studies specific to Turkey and findings of the reviewstudies.

the manufacturing location is changed to the Aegean regionof Turkey, and these parts are transported to construction siteby trucks covering a distance of 600 km.

Caduff et al. 2012 states that environmental impact pergenerated energy decreases as the turbine gets bigger.

Capacity factor is an important variablewhich defines the total energy production of the wind farmand hence affects the environmental impacts directly.

Another importantresult of the mentioned review is that impacts due to trans-portation have minor contributions on the total burdens(Arvesen and Hertwich 2012)

According to this review, for onshore wind farms,production of turbine components cause most of the emis-sions, which is in line with the results of this study as givenin Fig. 3 (Arvesen and Hertwich 2012)

Arvesen and Hertwich (2012) conducted an extensiveliterature review on LCA of energy generation from windpower by investigating 44 cases.

From another point of view the following evaluations arederived for the manufacturing and installation phase. Use ofnon-renewable energy sources, in other words, use of coal,natural gas and crude oil are the main contributors of ADPfossil. The main reasons for AP are nitrogen oxide and sul-phur dioxide emissions to atmosphere. Emission of nitrogenoxides to atmosphere, on the other hand, is mainly responsiblefor EP. Trichlorofluoromethane and dichlorotetrafluoroethane

Razdan and Garrett(2015) found similar environmental credits for impacts as92% of the steel, aluminium and copper originating from thewind farm was recycled, and the rest was sent to a landfillafter dismantling.

In another study dealingwith the whole life cycle stages of a wind farm, foundationwas not dismantled, all of the composite rotor wastes weresent to incineration; glass content was directed towards alandfill; and 20% of the rest was recycled (Xu et al. 2018).

A cra-dle-to-grave LCA study performed on a 50-MW onshorewind plant with a 20-year lifetime showed similar negativeimpacts for decommissioning phase (Garret and Ronde,2013)

The wind farm is an onshore facility located in the Marmararegion near Istanbul.

Characterization factors in CML 2001 methodologyare adopted to convert the flows into impact categories(Guinée et al. 2002)

In this sense, this study is the first one thatmodels the data obtained directly from an actual Turkishwind farm.

As of the end of 2019, Turkey has 198wind power plants with a total installed capacity of 8056MW in operation (TWEA 2020).

According to the data presented by theTurkish Wind Energy Association, currently 7.42% of thetotal Turkish electricity generation is originating from windfarms (TWEA 2020).

The 2015–2019 strategic plan prepared by theTurkish Ministry of Energy and Natural Resources statesthat financial incentives will be taken to encourage renew-able energy investments in Turkey (MoENR 2017).

Along withobtaining environmental performances of energy technolo-gies, LCA studies aid lowering the unwanted environmentalimpacts (Strantzali and Aravossis 2016), examining environ-mental trade-offs (Modahl et al. 2012) and evaluating decar-bonization potentials (Ramirez et al. 2020).

Although there are literature involving theenvironmental impacts of wind farms (Garrett and Rønde2013; Rashedi et al. 2013; Uddin and Kumar 2014; Var-gas et al. 2015) through LCA methodology as well as com-prehensive reviews of LCA of wind energy (Arvesen andHertwich 2012; Davidsson et al. 2012), it is a well-knownfact that obtaining reliable results depends on the usage ofsite-specific data.

Application of LCA methodology on various processes/products/services is known to create fruitful outcomes thatwill guide the decision-makers, manufacturers, researchersin developing sound strategies to lower the unfavour-able environmental impacts of such activities.

The facility in question is an onshore wind farm located in Turkey with a total installed capacity of 47.5 MWconsisting of 2.5 MW Nordex wind turbines.

Therefore, it is beneficial to employ holistic methodologiessuch as life cycle assessment (LCA) to investigate the pos-sible trade-offs between different impact categories.

Respect entails an understanding of the socioec-ological context of research as it relates to people and place.

There may be other considerations thatare context-specific to a particular culture, such as resourcestewardship institutions or responsibilities that have beendeveloped through years of experience and practice (Turnerand Berkes 2006; Reid et al. 2020)

scientists must recog-nize that Indigenous peoples have rights to self-determination,which extends to research partnerships and the creation anddissemination of new knowledge.

There is often an assumption –one we wish to avoid perpetuating here – that IK must be sub-sumed within Western scientific frameworks of knowledge,which can force Indigenous peoples to express themselves inways potentially contradictory to their own value and belief sys-tems (Nadasdy 1999). This practice can distort the accuracy andapplicability of IK, and is harmful to Indigenous ways of being.

Collaborative research with Indigenous partners requiresrecognition that science and scientists have in the past and con-tinue at present to (1) impose harm on Indigenous peoples; (2)discount IK; and (3) inappropriately reproduce, apply, or other-wise use information derived from IK (Pierotti 2012; Berkes2018)

Those seeking collaborations should be acutely aware thatclear tensions exist between IK and Western science epistemolo-gies.

Manyterritories in which cultural and knowledge transmission isongoing also tend to be remote and biodiverse, which posi-tion these title holders (that is, legal owners of land in thecontext of Indigenous laws) as natural biodiversity specialists(Figure 5; Garnett et al. 2018

This knowledge was in part derivedfrom how the area’s Haíɫzaqv people related to wolves; people ofthe territory also differ, depending on whether lineages originatefrom mainland or island areas.

Relational understanding was showcased in an example fromcoastal British Columbia, where IK holders shared knowledge oftwo wolf (Canis lupus) forms, locally referred to as “timberwolves” of the mainland and “coastal wolves” of the immediatelyadjacent offshore islands

The conclusions drawn from IK have interdisciplinary rele-vance as well.

Knowledge holders acrossdistinct cultures and environments accumulate information innumerous ways, including harvesting, observation, animalhusbandry, and experimentation, all supplemented by teach-ings from oral histories and cultural practices (Turner et al.2000; Berkes and Berkes 2009)

Research at the IK–science interface can benefit from thediversity inherent in IK approaches

Suchrecognition of system complexity (including synergistic and con-founding variables) is characteristic of IK, with the holistic viewsof ecosystems stemming in part from “relational” understandingsamong ecosystem components, including humans (Cajete 1995;Turner et al. 2000; Atleo 2011

considered by science, a reality supported by the fact thatIndigenous peoples themselves regularly form and testhypotheses (Cajete 1995; Atleo 2011).

Hypotheses constructed within the borders of scientificknowledge may be limited in complex or little-studied systems, aconstraint IK can address.

Indigenousways of knowing can shape and detail predictions not

Insights from IK can be relevant at many stages of theresearch process, including but not limited to project con-ceptualization and hypothesis development.

IK is often closely rooted in human survival and relation-ships between people and nature, and may furthermoretightly couple knowledge accumulation with cultural respon-sibility (Reid et al. 2020)

IK and science can share common properties andoffer complementary conceptual underpinnings

Anexample of how IK can provide information about healthand body condition comes from East Africa. In Kenya andSouth Sudan,

Eckert et al. (2018), forinstance, quantified size changes in yelloweye rockfish(Sebastes ruberrimus) based on historical accounts from theHaíɫzaqv, Kitasoo/Xai’xais, Nuxalk, and Wuikinuxv peoplesof western Canada.

In the meridianAmazon of Brazil, dos Santos and Antonini (2008), in docu-menting Enawene-Nawe knowledge of stingless bees, foundthat IK holders could discriminate among 48 different spe-cies and specify the ecological niche of each species.

Lee et al. (2018) coupled historical observations from theHaíɫzaqv First Nation of British Columbia with zooarchaeo-logical and scientific data to estimate northern abalone(Haliotis kamtschatkana) abundance on the Pacific coast ofCanada from the Holocene to the present.

Polfus et al. (2014) developed habitatmodels for woodland caribou (Rangifer tarandus caribou)based on IK from the Taku River Tlingit First Nation ofnorthern British Columbia, and showed a high degree ofsimilarity between resource selection functions (RSF) thatestimated habitat use derived from IK and collared caribou.

in HaíɫzaqvTerritory (coastal British Columbia), explic-itly guided by the Gvi’ilas (customary law) ofthe Haíɫzaqv people. The approach combinedHaíɫzaqv cultural values with their knowl-edge of bears, salmon, and people in animportant large watershed.

Place- basedknowledge of bear ecology guided theresearch design by informing the spatial

Drawing on millennia-old accumulation of knowledge andits contemporary recognition by others, IK has informed,enhanced, and complemented the study of ecology, evolu-tion, and related fields (Figure 2)

IK has been recognizedin the scholarly literature as having enriched understandingof a range of individual-level processes, including behavior(eg Bonta et al. 2017) and habitat selection (eg Polfus et al.2014)

IK has also contributed to the literature on population- toecosystem-level processes.

IK can also address processes at the community and ecosys-tem levels, including interspecific interactions (eg Wehi 2009)and ecosystem function (eg Savo et al. 2016)

IK has also contributed to understanding related to evolu-tion in many systems.

Understanding of physiology can also emerge from long-term observations, including harvesting and preparingplants and animals for food, medicine, shelter, clothes, andmore.

IK is distinct from science, localknowledge, and citizen science in that it includes not only directobservation and interaction with plants, animals, and ecosystems,but also a broad spectrum of cultural and spiritual knowledgesand values that underpin human–environment relationships(Berkes 2018)

IK in itsbroad scope also includes “Traditional Ecological Knowledge”(TEK) and “Indigenous Ecological Knowledge” (IEK) whenknowledge relates to ecology.

IK is generally thought ofas a body of place-based knowledges accumulated and transmit-ted across generations within specific cultural contexts.

Application ofthese broad and deep knowledges in a scientific context hasled to many contributions to the literature in ecology,evolution, and related fields

Despite its millennia-long and continued application by Indigenous peoples to environ-mental management, non- Indigenous “Western” scientific research and management have only recently considered IK.

Indigenous Knowledge (IK) is the collective term to represent the many place-based knowledges accumulated across generationswithin myriad specific cultural contexts.

It’s also timely for New York state, where floating solar could be considered as an alternative to terrestrial solar and is the source of debate and exploration.