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Record W4280614187 · doi:10.1111/jfb.15084

Gill area explains deviations from body size–metabolic rate relationship in teleost fishes

2022· article· en· W4280614187 on OpenAlexafffund
Anna Kuparinen, Darby Gielewski, Jeffrey A. Hutchings

Bibliographic record

VenueJournal of Fish Biology · 2022
Typearticle
Languageen
FieldEnvironmental Science
TopicPhysiological and biochemical adaptations
Canadian institutionsDalhousie University
FundersEuropean Research CouncilNatural Sciences and Engineering Research Council of CanadaHorizon 2020 Framework ProgrammeAcademy of Finland
KeywordsBiologyMetabolic rateLimitingFish <Actinopterygii>Metabolic activityEcologyFisheryAllometryZoologyPhysiologyEndocrinology

Abstract

fetched live from OpenAlex

Whether gill area constrains fish metabolism through oxygen limitation is a debated topic. Here, the authors provide insights into this question by analysing mass-specific metabolic rates across 44 teleost fishes extracted from FishBase. They explore whether species deviations from metabolic rates predicted by body mass can be explained by species gill area. They show that the gill area explains c. 26%-28% of species-level deviations from mass-specific metabolic rates. Their findings suggest that gill area might indeed be one of the factors limiting metabolic rate in fishes.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.002
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.027
GPT teacher head0.237
Teacher spread0.210 · 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 source (direct Gemma or distilled Codex), 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

Citations5
Published2022
Admission routes2
Has abstractyes

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