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When fish shoals meet: outcomes for evolution and fisheries

2003· article· en· W2148475738 on OpenAlexaff
Darren P. Croft, Jens Krause, Iain D. Couzin, Tony J. Pitcher

Bibliographic record

VenueFish and Fisheries · 2003
Typearticle
Languageen
FieldEnvironmental Science
TopicFish Ecology and Management Studies
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsShoalShoaling and schoolingFisheryEcologyBiologyGeographyOceanographyGeology

Abstract

fetched live from OpenAlex

Abstract The mechanisms underlying the social structure of free‐ranging fish shoals have received little attention in comparison to functional studies on shoaling. Recently, however, a number of investigations, both in the marine and in the freshwater environment, have begun to address the underlying mechanisms by concentrating on interactions between free‐ranging shoals. The rates of shoal encounters can influence the opportunities for individual assortment by phenotype and selection of shoal size; act as a constraint on the observed patterns of shoal structure by restricting individual choice behaviour and were found to be high in several small freshwater species (where intershoal distances were small), but lower in marine species where shoals were more dispersed. The duration of encounters may play a role in that it affects both the time available for assessment (of the encountered shoal) as well as that for exchange of individuals. Scarce published information on the outcome of shoal encounters suggests that the outcome of shoal encounters is influenced by shoal composition but not by shoal size. Individual behaviour may have evolved to maintain shoal size when shoals encounter, but when fish populations are depleted by fishing, this trait can exacerbate range and stock collapse. Furthermore, an understanding of the dynamics of shoal encounters has important consequences for the evolution of reciprocal altruism and the transmission of information through social learning within populations. Finally, information on encounter rates between shoals and the number of individuals that are exchanged on such occasions could be important for making predictions about the spread of disease through fish populations.

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 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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.161
Threshold uncertainty score1.000

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 studies0.0010.001
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 head0.012
GPT teacher head0.203
Teacher spread0.190 · 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 teacher head, not a consensus.

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

Citations44
Published2003
Admission routes1
Has abstractyes

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