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Record W4408091485 · doi:10.1016/j.newton.2025.100021

Physics and physiology of fish collective movement

2025· article· en· W4408091485 on OpenAlex
Yangfan Zhang, George Lauder

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

fundA Canadian funder is recorded on the work.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueNewton · 2025
Typearticle
Languageen
FieldEnvironmental Science
TopicFish Ecology and Management Studies
Canadian institutionsnot available
FundersOffice of Naval ResearchNatural Sciences and Engineering Research Council of CanadaCanadian Institutes of Health ResearchNational Science Foundation
KeywordsFish <Actinopterygii>Movement (music)PhysiologyPhysicsCognitive scienceBiologyFisheryPsychology

Abstract

fetched live from OpenAlex

The collective movement of animals has long been of interest to mathematicians, physicists, engineers, and biologists. This includes understanding the emergence of group motion from individual interactions, hydrodynamics of group locomotion, and biological processes that generate movement. Here, we discuss recent research on fish schools to understand how group movement alters hydrodynamics, energy use, and inter-individual interactions. Swimming in a school can save metabolic energy up to 56%–79% at higher swimming speeds when compared to swimming alone, and a clear minimum in the cost of transport exists at speeds around two body lengths per second, which is similar to swimming alone. We discuss that energetic savings result from a diversity of hydrodynamic mechanisms that are constantly changing as fish rearrange themselves within a school. A key goal of future research is to reveal potential global mechanisms of energetic savings in fish collective movement.

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.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.753
Threshold uncertainty score0.216

Codex and Gemma teacher scores by category

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)0.0000.000

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 head0.006
GPT teacher head0.210
Teacher spread0.204 · 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