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Record W2094435652 · doi:10.1098/rspb.2005.3096

Ontogeny of energy allocation reveals selective pressure promoting risk-taking behaviour in young fish cohorts

2005· article· en· W2094435652 on OpenAlexaff
Peter A. Biro, John R. Post, Mark V. Abrahams

Bibliographic record

VenueProceedings of the Royal Society B Biological Sciences · 2005
Typearticle
Languageen
FieldEnvironmental Science
TopicFish Ecology and Management Studies
Canadian institutionsUniversity of ManitobaUniversity of Calgary
Fundersnot available
KeywordsTroutPredationBiologyOntogenyVulnerability (computing)EcologyFish <Actinopterygii>Temperate climateHabitatTrade-offFisheryZoology

Abstract

fetched live from OpenAlex

Given limited food, prey fishes in a temperate climate must take risks to acquire sufficient reserves for winter and/or to outgrow vulnerability to predation. However, how can we distinguish which selective pressure promotes risk-taking when larger body size is always beneficial? To address this question, we examined patterns of energy allocation in populations of age-0 trout to determine if greater risk-taking corresponds with energy allocation to lipids or to somatic growth. Trout achieved maximum growth rates in all lakes and allocated nearly all of their acquired energy to somatic growth when small in early summer. However, trout in low-food lakes took greater risks to achieve this maximal growth, and therefore incurred high mortality. By late summer, age-0 trout allocated considerable energy to lipids and used previously risky habitats in all lakes. These results indicate that: (i) the size-dependent risk of predation (which is independent of behaviour) promotes risk-taking behaviour of age-0 trout to increase growth and minimize time spent in vulnerable sizes; and (ii) the physiology of energy allocation and behaviour interact to mediate growth/mortality trade-offs for young animals at risk of predation and starvation.

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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.011
Threshold uncertainty score0.323

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.001
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 head0.012
GPT teacher head0.228
Teacher spread0.216 · 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.

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

Citations130
Published2005
Admission routes1
Has abstractyes

Explore more

Same venueProceedings of the Royal Society B Biological SciencesSame topicFish Ecology and Management StudiesFrench-language works237,207