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Record W2157843309 · doi:10.1139/f02-001

Shifting gears: enzymatic evidence for the energetic advantage of switching diet in wild-living fish

2002· article· en· W2157843309 on OpenAlexvenueno aff
Graham D. Sherwood, Ivano Pazzia, Andrew Moeser, Alice Hontela, Joseph B. Rasmussen

Bibliographic record

VenueCanadian Journal of Fisheries and Aquatic Sciences · 2002
Typearticle
Languageen
FieldEnvironmental Science
TopicFish Ecology and Management Studies
Canadian institutionsnot available
Fundersnot available
KeywordsForagingTroutBiologyPredationPerchAnaerobic exerciseEcologyZoologyPredatory fishFish <Actinopterygii>Juvenile fishSalvelinusOntogenyJuvenileFisheryPhysiology

Abstract

fetched live from OpenAlex

Large variations in the activity and scaling patterns of enzymes involved in anaerobic metabolism exist and appear to be related to species differences in the locomotory habits of fish. Here, we show how the scaling of muscle lactate dehydrogenase (LDH) activity is highly variable in fish, not only among species, but also among populations of yellow perch (Perca flavescens) and lake trout (Salvelinus namaycush) exhibiting large differences in the scaling of fish activity costs. These differences in LDH scaling properties were significantly related to differences in diet ontogeny. Scaling coefficients and adjusted R 2 values of LDH versus body size relationships were both threefold higher in fish that do not make important diet shifts among planktivory, benthivory, and piscivory than in those that do. We argue that fish activity and related glycolytic potential are reset to lower values whenever fish are able to switch diet to larger prey while growing; we implicate the burst component of foraging (mostly attacks) as being responsible for changes in activity costs. Our results suggest that anaerobic power requirements in fish are highly plastic and adapted to local and recent food web conditions. We discuss these findings in relation to optimal foraging theory and the energetic basis of prey-size selection.

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 score0.119
Threshold uncertainty score0.895

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 communication0.0000.001
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 head0.037
GPT teacher head0.241
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

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
Published2002
Admission routes1
Has abstractyes

Explore more

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