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Record W3033601065 · doi:10.1089/zeb.2019.1851

Collective Behavior in Wild Zebrafish

2020· article· en· W3033601065 on OpenAlexaff
Delia S. Shelton, Sierra G. Shelton, Danita K. Daniel, Manickam Raja, Anuradha Bhat, Dennis M. Higgs, Emı́lia P. Martins

Bibliographic record

VenueZebrafish · 2020
Typearticle
Languageen
FieldEnvironmental Science
TopicFish Ecology and Management Studies
Canadian institutionsUniversity of Windsor
FundersNational Institute of Environmental Health Sciences
KeywordsZebrafishDanioBiologyGroup behaviorFish <Actinopterygii>Collective behaviorSalientShoaling and schoolingAnimal behaviorEcologyFisheryZoologyGeneticsSocial psychologyGeographyPsychologyGene

Abstract

fetched live from OpenAlex

Anthropogenic change is expected to alter environments at alarming rates. To predict the impact of modified environments on social behavior, we must study the relationship between environmental features and collective behavior in a genetically tractable model, zebrafish ( Danio rerio ). Here, we conducted a field study to examine the relationship between salient environmental features and collective behavior in four populations of zebrafish. We found zebrafish in flowing water formed volatile groups, whereas those in still water had more consistent membership and leadership. Groups in fast-flowing water were large (up to 2000 fish) and tightly knit with short nearest neighbor distances, whereas group sizes were smaller (11 fish/group) with more space between individual fish in still and slow-flowing water. These observations point to a possible profound role of water flow in influencing collective behavior in wild zebrafish.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.013
Threshold uncertainty score0.027

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.014
GPT teacher head0.216
Teacher spread0.202 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations46
Published2020
Admission routes1
Has abstractyes

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