MétaCan
Menu
Back to cohort
Record W2099226056 · doi:10.1242/jeb.205.7.1031

Relationship between individual variation in morphological characters and swimming costs in brook charr (<i>Salvelinus fontinalis</i>) and yellow perch (<i>Perca flavescens</i>)

2002· article· en· W2099226056 on OpenAlexaff
Patrice Boily, Pierre Magnan

Bibliographic record

VenueJournal of Experimental Biology · 2002
Typearticle
Languageen
FieldEnvironmental Science
TopicFish Ecology and Management Studies
Canadian institutionsUniversité du Québec à Trois-Rivières
Fundersnot available
KeywordsFontinalisSalvelinusPerchBiologyZoologyPercidaeVariation (astronomy)FisheryFish <Actinopterygii>EcologyTrout

Abstract

fetched live from OpenAlex

The objective of this study was to examine if individual variation in morphological characters is related to swimming costs in wild and domestic brook charr, and in wild yellow perch. Our results indicate that absolute swimming cost was higher in wild and domestic brook charr individuals having a stout body shape, and these individuals are therefore less efficient swimmers. These results are consistent with field observations that described relationships between individual variation in morphology and habitat use in salmonids. Further analyses indicated that standard metabolic rates were higher in individuals having a stout body shape, and that net swimming cost was not related to body shape. Accordingly, the higher swimming cost of stout individuals is probably an indirect consequence of an increase in standard metabolic rate. In wild yellow perch, absolute and net swimming costs were higher in individuals having a stout body shape and a low aspect caudal fin, and standard metabolic rate was not related to body shape. Therefore, in contrast to brook charr, individual variation in the swimming cost of yellow perch appears to be related to morphological characters that affect drag and thrust forces, which is consistent with previously published inter-specific observations.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.021
Threshold uncertainty score0.480

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.034
GPT teacher head0.266
Teacher spread0.231 · 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

Citations93
Published2002
Admission routes1
Has abstractyes

Explore more

Same venueJournal of Experimental BiologySame topicFish Ecology and Management StudiesFrench-language works237,207