Nothing in between. A model that arrives at the correct answer through careful reasoning receives the same reward as one that guesses correctly by chance.

WeirdML V2 places models in an unusually resource-constrained environment: models get only five attempts to submit working code, with no access to external tools. This setup has not been the focus of recent RL training.

SWE-chat is a living dataset; our collection pipeline automatically and continually discovers and processes sessions from public repositories

Luna conducted roughly 20 interviews on Google Meet with the camera off. Hired 2 full-time employees after 5-15 minute calls, and rejected CS and physics students for lacking retail experience.

Add screenshot-based LLM judge evaluator, screenshot collector, and --parallelize flag

We develop reliable a posteriori error estimators for fully discrete Runge–Kutta discontinuous Galerkin approximations of nonlinear convection–diffusion systems endowed with a convex entropy in multip

using SemanticCommit, we recorded all instances of edits, check for conflicts, make change, local, and global resolution actions using telemetry.

A sub-task was considered a failure if the participant was unable to complete it within the time limit.

Finally, we conducted an informal interview about their experience.

After both tasks were completed, participants filled out a final survey to compare the two conditions.

We chose GPT-4o for performance and latency reasons, as it performed optimally against our evals.

We also ran evaluations of model latency and classification performance under varying false positive rates for the following LLMs by OpenAI: GPT-4o, GPT-4o-mini, and o3-mini.

For each task, participants were tasked with integrating three new pieces of information into the memory, one at a time ("sub-tasks").

We ensured each list was 30 items long as our pilot studies suggested this was long enough that manual detection starts to become unwieldy (users need to scroll up and down the document), but short enough that participants could become familiar in a short period.

We adapted two intent specifications from our evals: Mars Game Design Document and Financial Advice AI Agent Memory, as these tasks mapped to the two paradigmatic types covered in Sections 2 and 2.1 (design documents, and AI memory of the user).

We recruited 12 participants (7 female, 5 male) through the mailing lists of two research universities and one multinational technology company.

We chose OpenAI's ChatGPT Canvas as a baseline for five reasons: (i) it is a popular, commercially available tool, hence it is likely familiar to users; (ii) it provides a document editing view, where users can select text and ask GPT to rewrite it, or chat with an AI to make global edits; (iii) it employs a similar class of model (GPT-4o); (iv) it supports similar editing features as SemanticCommit like inline text selection, conflict highlighting, and a diff view, while adding free-form editing; and (v) similar interfaces like Anthropic Artifacts tended to rewrite the specification entirely, and did not offer Canvas's "diff" view to allow for a fair comparison.

With participant consent, we recorded audio and screen-casts, and participants were encouraged to think aloud.

Four coauthors created the evals, and two coauthors manually double-checked all conflicts, a process that took several days.

We ran one pilot study with five users of our card-based interface, and a second with four users of a revised interface.

Our explorations went through substantial iterations and prompt prototyping over a period of eight months, evolving in response to two pilot studies and progressing from a card-based interface to a list of texts.

We iterated on prompts using ChainForge [5] by setting up an evaluation pipeline against our datasets, which allowed us to observe the effects of prompt changes and model choices.

To measure statistical significance, we used Mann–Whitney–Wilcoxon tests and report the p-values.

For qualitative analysis, the first author performed open coding on participant responses and audio transcripts to identify themes, which were used to interpret the qualitative results.

In the post-task surveys, we collected self-reported NASA Task Load Index (TLX) scores, Likert-scale ratings for ease of use, and responses on how well the AI helped participants identify, understand, and resolve semantic conflicts.

Each condition had a time limit of 15 minutes, after which the participant completed a post-task survey.

Before each task, participants received a tutorial on the assigned tool and were given five minutes to explore it using a test document.

Both the order of task assignment and tool assignment were counterbalanced and randomly assigned.

We conducted a controlled within-subjects study with mixed methods, comparing SemanticCommit with a baseline interface.

We run end-to-end on our four eval datasets using GPT-4o and GPT-4o-mini and report the mean ± stddev for accuracy, precision, recall, and F1 scores for the three approaches in Figure 5.

We compare our end-to-end system against two simpler methods: (i) DropAllDocs, which adds all documents to the context for conflict classification; and (ii) InkSync [56] which generates a JSON list of string-replace operations.

In order to minimize relevance assessment issues, we apply a PageRank-based relevance ranking over the KG, akin to HippoRAG [36].

We implement the back-end using a knowledge-graph (KG) RAG architecture [36] consisting of two phases: pre-processing and inference.

Through a within-subjects study with 12 participants comparing SemanticCommit to a chat-with-document baseline (OpenAI Canvas), we find differences in workflow: half of our participants adopted a workflow of impact analysis when using SemanticCommit, where they would first flag conflicts without AI revisions then resolve conflicts locally, despite having access to a global revision feature.

We implement the back-end using a knowledge-graph (KG) RAG architecture [36] consisting of two phases: pre-processing and inference.

In order to minimize relevance assessment issues, we apply a PageRank-based relevance ranking over the KG, akin to HippoRAG [36].

We compare our end-to-end system against two simpler methods: (i) DropAllDocs, which adds all documents to the context for conflict classification; and (ii) InkSync [56] which generates a JSON list of string-replace operations.

We run end-to-end on our four eval datasets using GPT-4o and GPT-4o-mini and report the mean ± stddev for accuracy, precision, recall, and F1 scores for the three approaches in Figure 5.

We conducted a controlled within-subjects study with mixed methods, comparing SemanticCommit with a baseline interface.

Both the order of task assignment and tool assignment were counterbalanced and randomly assigned.

Before each task, participants received a tutorial on the assigned tool and were given five minutes to explore it using a test document.

Each condition had a time limit of 15 minutes, after which the participant completed a post-task survey.

In the post-task surveys, we collected self-reported NASA Task Load Index (TLX) scores, Likert-scale ratings for ease of use, and responses on how well the AI helped participants identify, understand, and resolve semantic conflicts.

For qualitative analysis, the first author performed open coding on participant responses and audio transcripts to identify themes, which were used to interpret the qualitative results.

To measure statistical significance, we used Mann–Whitney–Wilcoxon tests and report the p-values.

We iterated on prompts using ChainForge [5] by setting up an evaluation pipeline against our datasets, which allowed us to observe the effects of prompt changes and model choices.

Our explorations went through substantial iterations and prompt prototyping over a period of eight months, evolving in response to two pilot studies and progressing from a card-based interface to a list of texts.

We ran one pilot study with five users of our card-based interface, and a second with four users of a revised interface.

Four coauthors created the evals, and two coauthors manually double-checked all conflicts, a process that took several days.

With participant consent, we recorded audio and screen-casts, and participants were encouraged to think aloud.

We process this data in a three-stage pipeline (Figure 6). In the first stage, Sentence Segmentation and Categorization, abstracts are split into individual sentences using the NLTK package, and each sentence is classified into one of the five pre-defined aspects as listed in Section 4.1.1. Classification is performed by prompting an LLM (see prompt used in Appendix D.1) with the sentence and its full abstract.

Then, we segment sentences within each aspect into grammar-preserving chunks (see prompt used in Appendix D.2). This results in grammatically coherent chunks that are the basis of structure patterns. After identifying chunk boundaries, we again prompt an LLM to generate labels for chunks in a human-in-the-loop approach: starting from an initial set of labels for chunk roles, when a new label is generated, a researcher from the research team examines the new label and merges it with existing labels if appropriate, controlling for the total number of labels.

In this study, we allowed participants to experience views of same-aspect sentences (Section 4.1.1) with different combinations of highlighting, ordering, and alignment (as described in Section 4.1.2 and Section 4.1.4) enabled or not, in order to understand which and/or what combinations most effectively supported users' ability to skim and read laterally across documents.

Inspired by GP-TSM [24], AbstractExplorer first segments sentences into grammar-preserving chunks—segments that respect grammatical boundaries, i.e., an LLM judges that the sentence can be truncated at that chunk boundary without breaking the grammatical integrity of the preceding text. Each chunk is then classified by an LLM as having one of nine pre-defined roles, each of which has its own assigned color.

AbstractExplorer classifies sentences into five pre-defined aspects common in CHI abstracts: Problem Domain, Gaps in Prior Work, Methodology/Contribution, Results/Findings, and Discussion/Conclusion.

In the context of close reading of research paper abstracts at scale, our findings suggest AbstractExplorer enabled participants to scale up the number of papers they could review through efficient skimming and find common patterns and outliers through sentence comparison, resulting in a rich synthesis of ideas and connections to foster deeper engagement with scholarly articles.

We extend existing approaches through automated role annotation, establishing alignments using grammatical chunk boundaries, and preserving sentences in their entirety, instead of relying on abstract meta-data.

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

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.

The front-end React app allows users to view partially loaded custom aspects while they are being generated.

Custom aspects are generated dynamically via API calls to a FastAPI back-end, which prompts an LLM to check whether each sentence in the filtered subset matches the aspect description—either in terms of overall content or a matching token—and extracts the most relevant chunk of that sentence to highlight.

All the chunks and corresponding labels are pre-computed and stored as JSON files, ensuring responsiveness and low latency of the web application.

After obtaining an expanded set of high-level chunk labels, we assign them to each of the sentence chunks by using LLMs in a multi-class classification few-shot learning task, with the initial labels and assignment as examples.

After identifying chunk boundaries, we again prompt an LLM to generate labels for chunks in a human-in-the-loop approach: starting from an initial set of labels for chunk roles, when a new label is generated, a researcher from the research team examines the new label and merges it with existing labels if appropriate, controlling for the total number of labels.

Then, we segment sentences within each aspect into grammar-preserving chunks.

Classification is performed by prompting an LLM with the sentence and its full abstract.

In the first stage, Sentence Segmentation and Categorization, abstracts are split into individual sentences using the NLTK package, and each sentence is classified into one of the five pre-defined aspects as listed in Section 4.1.1.

We process this data in a three-stage pipeline (Figure 6).

From this pane, users can toggle and view highlighted sentence chunks, click to scroll to the relevant abstract for each sentence, or remove bookmarks from the list.

When users click on a bookmark icon to the left of any specific sentence in the Cross-Sentences Relationships Pane, that sentence is added to a bookmark list that can be viewed in the Bookmarked Sentences alternate pane.

Our user studies on 24 participants demonstrate the usability of our proposed virtu.

Sentence bookmarking helps users keep track of papers to revisit later.

To address this, we conducted a systematic review and meta-analysis of 56 papers p.

Users can enter a search term into the search bar and only papers that include that exact term will appear in the Cross-Sentence Relationships pane.

Through two user studies, we found that with an accompanying size-ch.

Filtering enables users to narrow their focus to a subset of the corpus while still benefiting from features that help them recognize cross-sentence relationships within the remaining abstracts.

Through a VR experiment (N=31) and a following online survey (N=142).

The Abstracts panel can be customized by users to display the full abstract text, an abstract “TLDR” (a shorter abstractive summary generated by an LLM), or both at the same time.

To allow users to contextualize individual sentences within their respective abstracts, we link the Cross-Sentence Relationship and Abstract panels: when users click on any sentence in the Cross-Sentence Relationships pane, the corresponding full abstract is automatically highlighted and scrolled into view in the Abstracts panel, offering additional context when needed.

methods, we conducted a controlled experiment (N=24) to assess the user performance, experience and VR sickness.

Together, the vertical and horizontal juxtapositions are designed to help users identify both high-level commonalities and nuanced variations across structurally similar sentences.

ryday life, we ran three co-design workshops (N=69).

If users prefer, sentences can be toggled to a left-justified view to facilitate conventional reading and skimming.

In our mixed-method study with 21 participants, we evaluated the usability and effectiveness.

This may effectively decrease context switching and lead to more robust mental models without requiring more cognitive load.

These alignment options are intended to enable users to more easily read analogous chunks across sentences from different abstracts, ignoring details serving other roles within the sentence.

ucted on eight-month longitudinal user study with 12 older adults aged 60 years and above to demonstrate the eff.

By default, sentences are vertically aligned by the middle of their shared structure tuple, but users can freely switch between the three alignment options using the button group atop the Cross-Sentence Relationship pane.

In a study (N=18), we compared our multimodal technique with pseudo-haptics al.

AbstractExplorer also aligns the sentences in three different ways, as illustrated in Figure 5: vertical alignment by the middle of the structure tuple (second element), vertical alignment by the left of the structure tuple (first element), and left-justified alignment (horizontal juxtapositions).

Through a study with 30 participants, we show that the system enables users to perc.

This ordering prioritizes dominant structural patterns (largest groups first) while exposing fine-grained variations (via length-sorted triplets), mirroring how humans compare sentences, if SMT is an accurate description in this domain of comparative close reading.

ploratory and a confirmatory factor analysis with a total of 159 participants.

Within each of those groups, sentences are arranged by increasing length.

We conducted an experiment with 24 participants using a real vehicle along a route with known.

Sentences are recursively grouped by sequences of three chunk roles, with groups ordered by decreasing size.

We conducted a study involving 32 participants organized in teams of two.

AbstractExplorer orders sentences within each structure group based on the sequential pattern of chunk roles (vertical juxtapositions).

In a within-subjects study (20 participant pairs), we examined the effects of vibrotactile hap.

This allows users to first understand the different structure patterns and their commonality, before diving into close reading at scale of the sentences that share a particular structure by clicking any of the “Expand” toggles.

Through task-based experiments with 25 pairs of participants (50 individuals), we evaluated quanti.

An example sentence is also shown alongside each structure.

The total number of sentences with each structure in the (possibly filtered) corpus is shown in parentheses and visualized as a histogram.

In AbstractExplorer, sentences are grouped by this definition of sentence structure.

These role-based color highlights enable quick identification of analogous chunks and visual pattern matching over sequences of chunks across sentences.

Each role label has a corresponding unique color shown at the top of the “Cross-Sentence Relationship” panel.

We adopt the color palette of Tableau 10, which was carefully designed to be clearly distinguishable.

Each chunk is then classified by an LLM as having one of nine pre-defined roles, each of which has its own assigned color.

AbstractExplorer first segments sentences into grammar-preserving chunks—segments that respect grammatical boundaries, i.e., an LLM judges that the sentence can be truncated at that chunk boundary without breaking the grammatical integrity of the preceding text.

We conducted five user studies with 98 participants.

Viewing one aspect at a time enables users to closely read and compare just the analogous sentences of abstracts, which may be cognitively easier than the comparative close reading of many abstracts in their entirety, especially if cross-sentence relationships are pre-computed and reified in the interface.

AbstractExplorer classifies sentences into five pre-defined aspects common in CHI abstracts: Problem Domain, Gaps in Prior Work, Methodology/Contribution, Results/Findings, and Discussion/Conclusion.

We chose the sentence as our unit for cross-document alignment because: (1) it preserves complete propositional content (unlike phrases or words), (2) maintains grammatical coherence when isolated (unlike arbitrary text spans), and (3) serves as the minimal self-contained unit where aspects can be meaningfully compared.

To keep details at the forefront of the interface, we designed a mechanism to slice abstracts for viewing them from specific angles, allowing for comparative close reading at scale at the sentence level.

To do this, ABSTRACTEXPLORER is designed to support close-reading purpose-defined slices through a large collection-1,057 in our studies-of paper abstracts.

ABSTRACTEXPLORER is designed to help researchers (1) skim, read, and better familiarize themselves with the contents and composition style of a large corpus of abstracts and (2) reason about cross-paper relationships at scale without abstracting away the author-written sentences about their own work.

Finally, a summative study (Section 6) describes how researchers used ABSTRACTEXPLORER to familiarize themselves with a corpus of ~1000 CHI paper abstracts—reading across a larger and more diverse collection of abstracts and more easily discerning relationships and distributions across prior work.

Second, an ablation study with eye-tracking (Section 5) revealed that the three key features of ABSTRACTEXPLORER's central cross-sentence relationships pane-sentence order, role-coordinated highlighting, and alignment-work best in concert, not alone.

Three studies inform and validate ABSTRACT EXPLORER's design: First, a formative study (Section 3) suggested unmet needs and interest in our approach to supporting cross-document reasoning.

We demonstrate these features’ utility in the context of helping researchers’ skim and read closely and laterally across a corpus of scientific abstracts.

AbstractExplorer instantiates new minimally lossy SMT-informed techniques for skimming, reading, and reasoning about a corpus of similarly structured short documents: phrase-level role classification that drives sentence ordering, highlighting, and spatial alignment.

A summative study (N=16) describes how these features support users in familiarizing themselves with a corpus of paper abstracts from a single large conference with over 1000 papers.

An ablation study (N=24) validated that these features work best together.

AbstractExplorer has a unique combination of LLM-powered (1) faceted comparative close reading with (2) role highlighting enhanced by (3) structure-based ordering and (4) alignment.

We compose and evaluate a system, called AbstractExplorer, with analogous SMT-derived characteristics for the domain of scientific abstract corpus familiarization.

to the waysthat documents were organized in archives,

Such actions include, for example,enhancing microbial activity by adding appropriateelectron donors or acceptors to the system (29), orintroducing abiotic reactants into contaminatedgroundwaters such as zero-valent metals in per-meable reactive barriers (30)

But I am sure that hierarchies can get in the way, especially at the beginning.

a hybrid system, as I kept these notes in close connection with Ecco Pro, which I used to keep an index of the notebook material.

Put ideas on index cards – one to a card – and then arrange them in differ-ent structures. Again, you can do this in a series of passes, using a differentcriterion each time; this will help you to identify core concepts, structuresand outliers.

Finding the notes This will become problematic with larger volumes. For the most part, two tools suffice for me: 1) an alphabetical index; 2) notes on the bibliographical slips, in case the problem arises from the name.

What belongs together changes anyway depending on the question being asked and cannot be predetermined schematically. No straitjacket, but rather the principle of arbitrariness

The cross-referencing technique solves all organizational problems. Misplacements must be corrected by cross-referencing, not by rearranging.

Each note must have a fixed location that is never changed, as finding it depends on this. Remove it when needed and replace it exactly where it was placed This requires numbering the slips of paper. There will be long numbers, therefore alternating between numbers and letters for quicker recognition: 533/15 d 17 a 1

THE SCIENCE OF THE FILING ENGINEERThe Simplex Alpabetic Method Is Considered the Most Efficient and Takes Care ofTAverage Requirements - It May Be the 95% File-Complex Methods Also Explained

The next Complex method in order, is the Numeric,which may be divided into three classes, straight num-eric, duplex and decimal. It is safe to say that withthe straight Alphabetic or Geographic, ninety-five per-cent of the cases where an Index is used will be moreefficiently handled by the use of either one of theseMethods, than by the Numeric. However, there aresome cases where there is a great deal of cross refer-ence, thus making the use of the Numeric methodmore advantageous.

Classification of Files of the U. S. ExplosivePlant at Nitro, W. Va.By Miss J. L. Dillard

UBJECT filing is a branch of filingwhich is not much used in thebusiness world but which deservesas much attention as any other. It ismore complicated than other systemsand an adaptation has to be workedout for each , business to which it isapplied, but after the classification hasonce been made, it will prove to be foreconomy and increased efficiency.

ll- cause mortality 0.77(0.73 to 0.80)

The participants were the English as a foreign language (EFL) teachers and students of Class VIII from astate-run school of Paschim Medinipur district, West Bengal, India. The school was chosen randomly bythe researchers. The data was collected from December 2019 to March 2020. The researchers visited theinstitution thrice in a week during the abovementioned period. Three teachers and sixty students fromSection A and B participated in this study.

Elevational declines occur in both directions from the continental divide that separates Banff and Yoho National Parks and these lower elevation areas contain higher densities of many species, especially ungulates in the east end of Banff

Banff and Yoho National Parks

Semistructured hour-longinterviews addressed interpretations of illness, self-carepractices, and use of and access to health care. Inter-views were tape-recorded and transcribed verbatim.Respondents were interviewed in their language ofchoice, either Tagalog or English. With the exceptionof 2 respondents who were interviewed in English,respondents were interviewed in Tagalog by a fluentTagalog speaker.

Over six thousand leaves (which is tosay, thirteen thousand pages) survive, and experts estimate that thisrepresents about a quarter of the original total. This implies that Leonardofilled his notebooks at the rate of about a thousand pages a year, allobsessively covered with drawings, diagrams and idiosyncratic mirrorhandwriting. ‘I worked out at one point that he must have written aboutfifty academic-length books, if you put them all together,’ says Kemp. ‘Hewas never at rest.’

GENERAL METHODS

Experimental evidence sug-gests shading similar to that expected from FPV may lead toincreased phytoplankton biomass and reduced macrophytebiomass,50 though this remains to be tested

Using ourmeasured emissions per kWh, we can estimate that at present,FPV-derived GHG emissions from waterbodies are 6.7 GgCO2-eq year−1 (assuming ∼1000 kWh kWp−1). At modeledpractical potential generation of 9434 TWh year−1, FPV-derived waterbody GHG emissions may increase to 24.6 TgCO2-eq year−1.

we estimate a 26.8% increase in greenhouse gasemissions following FPV installation using a carbon dioxide-equivalent basis.

Rates of bubble accumulation were similarbetween pond types (p = 0.955; Figure S4A), so any changesin CH4 ebullition associated with FPV installation must havebeen driven by differences in bubble CH4 concentration�indeed, the CH4 concentration in bubble trap headspace inponds with FPV (60.0 ± 4.70% CH4) was nearly twice as highas in ponds without FPV (34.4 ± 4.00% CH4; p < 0.001;Figure S4B).

Combin-ing measured dissolved gas concentrations and k600 values toestimate diffusive CO2 and CH4 flux, we found that, onaverage, whole-pond diffusive CO2 emissions were 23.6 ±7.50% lower and diffusive CH4 emissions were 17.5 ± 25.1%lower following FPV deployment (Figure 6A and Table S4).

Using a BACI approach, we demonstrate that FPV deploymentwith 70% coverage led to increased pond GHG emissionswithin days of deployment, and this effect lasted for weeks tomonths. Increased emissions were driven by greater CH4ebullition which offset reduced diffusive CO2 and CH4emissions in FPV-covered ponds.

wecalculated whole-pond diffusive flux, assuming edge area forboth control and treatment ponds was 270 m2, the pond centersurface area for control ponds was 630 m2, pond center surfacearea for treatment ponds was 270 m2 (this subtracts the totalarea of FPV array that is in physical contact with the watersurface), and that fluxes were constant over a 24 h period.

We compared GHG dynamicsbetween ponds with and without FPV using a mixed modelapproach in R Statistical Software following a BACIapproach.

measuring linear rates ofCO2 and CH4 accumulation (or depletion) in a floatingchamber (18.93 L; 0.071 m2 cross-sectional area) connected toa cavity-ringdown spectroscope (Los Gatos, Inc.) for 5 minand collecting surface water and air samples for analysis of CO2and CH4 concentrations from the same location immediatelyafter the 5 min incubation period as described previously

Diffusive exchange of dissolved gases between ponds and theatmosphere (mmol m−2 h−1) can be calculated from dissolvedgas concentrations as35k C Cdiffusive flux ( )x water air=where Cwater and Cair indicate the gas concentration (μmol L−1)in the water and atmosphere, respectively

We calculatedebullitive flux asVebullitive flux CH bubble volumefunnel area time4m= [ ] ×× ×where [CH4] is the concentration of CH4 in the trap (μL L−1)and Vm is the molar volume of gas at standard conditions (22.4L mol−1).

We deployed passive bubbletrap samplers from May to October 2023 to measure rates ofebullitive CH4 flux.

Wecalculated dissolved gas concentrations using constantsdetermined by Weiss32 and Wiesenburg and Guinasso.

using a gas chromatograph equipped with a flameionization detector and autosampler (Shimadzu GC 2014).

We sampled for dissolved GHGconcentrations in pond surface water on two occasions in2022, and 14 occasions in 2023 using a headspace equilibrationapproach.

We characterized the temperature and dissolved oxygenconcentrations of the water column in each pond using athermistor and an optical dissolved oxygen sensor attached to aManta +35 or a Manta +20 instrument (Eureka Water Probes,Austin, TX).

Floating solar arrays (Ciel etTerre International, France) were deployed on three ponds:the FPV array on pond 124 was constructed from June 15−29,2023, pond 123 from June 29 to July 14, 2023, and pond 125from September 18−28, 2023.

Using these measure-ments, we calculated diffusive CO2 and CH4 emissions andcompared total GHG emissions between ponds with andwithout FPV.

We measured water column temperature,dissolved oxygen saturation, and dissolved CO2 and CH4concentrations in surface and bottom waters, quantified ratesof CH4 ebullition, and determined treatment-specific air−watergas exchange rates (i.e., k600 values)

We deployed FPV arrays on constructed ponds at the CornellExperimental Pond Facility in New York, USA in summer2023 (Figure 1). Arrays were designed to maximize powerproduction potential and thus also potential impacts (70%panel coverage)

Here, we report results from the first two years of anecosystem-scale experiment used to test the effect of FPVdeployment on GHG dynamics and atmospheric GHGexchange in pond

Here, we usean ecosystem-scale experiment to assess how GHG dynamics in ponds respond toinstallation of operationally representative FPV

. In order to establish the mentionedcountry-specific database, a national strategy should belaunched.

The GWP results of the base scenario of this study is 5.24g CO 2 eq/kWh.

Realdata based on monthly energy generation collected from awind farm for a year indicates capacity factors ranging from21 to 64% with an average value of 40%.

The results obtained by changing the recycle ratioof metals at EoL are given in Fig. 5.

In this part of the study, two scenarios involving the trans-portation of the main units during the construction phase andthe metal recycling ratios at end-of-life (EoL) are analysed.

The main uncertainty in thisLCA is the considered amount of recycling in the futureafter decommissioning the wind farm, and the impact of thisuncertainty on the results is handled by performing scenarioanalysis for various recycling ratios.

Electricity consumption during the manufacturing andinstallation stage is strictly measured by the service providerto determine the cost. Similarly the exact amount of dieselconsumption is obtained from the facility records.

The contribution of various phases of life cycle to environ-mental impact categories are shown in Fig. 3.

The LCA study is performed as given in ISO 14040/14044standards (ISO 2006a, b). Therefore, goal and scope defini-tion, inventory analysis, impact assessment and interpreta-tion are conducted in an iterative way.

The objective of this study is to apprise the envi-ronmental impacts of a full-scale wind farm via LCA meth-odology in a cradle to grave scope.

The study by Jianget al. (2018) examines the environmental impacts of gearboxvia LCA.

. LCA is used to examine the environ-mental impacts of a wind farm with 76 turbines of 1.5 MWin another study (Ozoemena et al. 2018)

The aim of this study is to investigate the environmental impacts of a full-scale wind farm using life cycle assessmentmethodology.

We therefore adviseresearchers to earn trust and foster healthy working relation-ships with Indigenous peoples to determine research prioritiesand agreements long before data collection begins (Lake et al.2017)

Research design should then unfold in acollaborative and transparent manner, with input from IKholders (Adams et al. 2014

At the onsetof collaborative studies, scientists should first develop researchagreements with Indigenous peoples in whatever form islocally appropriate, a step independent of any institutionalethics approvals

McBride et al. (2017) usedParticipatory Geographic Information Systems that drew uponand analyzed IK observations from Indigenous peoples acrossthe US related to fuel load, forest type, and burn severity.

trackers could provideauxiliary natural history data whereas radio tracking waslimited solely to data on movement.

Attum et al. (2008) demon-strated that estimates of Egyptian tortoise (Testudo klein-manni) home ranges in North Sinai, Egypt, derived fromradio telemetry were in agreement with estimates byIndigenous people, who tracked tortoises on foot,

In the example mentioned above,Riedlinger and Berkes (2001) also described how Inuit observa-tions and hypotheses of climate change in northern Canadacould account for multiple interacting variables and ecologicalcomplexity, such as climate variability and sea-ice break up.