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Record W2005545059 · doi:10.1098/rspb.2006.3741

Little left in the tank: metabolic scaling in marine teleosts and its implications for aerobic scope

2006· article· en· W2005545059 on OpenAlexafffund
Shaun S. Killen, Isabel Costa, Joseph A. Brown, A. Kurt Gamperl

Bibliographic record

VenueProceedings of the Royal Society B Biological Sciences · 2006
Typearticle
Languageen
FieldEnvironmental Science
TopicPhysiological and biochemical adaptations
Canadian institutionsMemorial University of Newfoundland
FundersNatural Sciences and Engineering Research Council of CanadaMemorial University of Newfoundland
KeywordsScope (computer science)Fish larvaeBiologyFish <Actinopterygii>ScalingEcologyOntogenyMarine lifeFunction (biology)Marine fishAerobic exerciseAdaptation (eye)Life history theoryLarvaLife historyEvolutionary biologyFisheryComputer scienceMathematicsNeuroscienceEndocrinology

Abstract

fetched live from OpenAlex

Fish larvae are the world's smallest vertebrates, and their high rates of mortality may be partially owing to a very limited aerobic scope. Unfortunately, however, no complete empirical dataset exists on the relationship between minimal and maximal metabolism (and thus aerobic scope) for any fish species throughout ontogeny, and thus such an association is hard to delineate. We measured standard and maximal metabolism in three marine fish species over their entire life history, and show that while aerobic scope depends greatly on body size and developmental trajectory, it is extremely small during the early life stages (factorial aerobic scope < or =1.5). Our findings strongly suggest that limited scope for aerobic activity early in life is likely to constrain physiological function and ultimately impact behaviour and possibly survival. Furthermore, our results have important implications for ecological models that incorporate metabolic scaling, and provide additional evidence against the existence of 'universal' scaling exponents.

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 score0.374
Threshold uncertainty score0.276

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 studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.023
GPT teacher head0.244
Teacher spread0.221 · 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

Citations257
Published2006
Admission routes2
Has abstractyes

Explore more

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