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Record W6911016655 · doi:10.5061/dryad.866t1g1qm

The effect of temperature on appetite in fish

2021· dataset· en· W6911016655 on OpenAlexaff

Bibliographic record

VenueOpen MIND · 2021
Typedataset
Languageen
Field
Topic
Canadian institutionsUniversity of New Brunswick
Fundersnot available
KeywordsEctothermSpecific dynamic actionPostprandialOxygenAppetiteScope (computer science)Fish <Actinopterygii>Consumption (sociology)

Abstract

fetched live from OpenAlex

Temperature has a dramatic effect on the physiology of ectothermic animals, impacting most of their biology. When temperatures increase above optimal for an animal, their growth gradually decreases. The main mechanism behind this growth rate reduction is unknown. Here, we suggest the ‘aerobic scope protection’ hypothesis as a mechanistic explanation for the reduction in growth. After a meal, metabolic rate, and hence oxygen consumption rate, transiently increases in a process called specific dynamic action (SDA). At warmer temperatures, the SDA response usually becomes temporally compressed, leading to a higher peak oxygen consumption rate. This peak in oxygen consumption rate risks taking up much of the animal’s aerobic scope (the difference between resting and maximum rates of oxygen consumption), which would leave little residual aerobic scope for other aerobic functions. We propose that water-breathing ectothermic animals will protect their postprandial residual aerobic scope by reducing meal sizes in order to regulate the peak SDA response during times of warming, leading to reductions in growth. This hypothesis is consistent with the published literature on fishes, and we suggest predictions to test it.

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)0.006
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Dataset · Consensus signal: Dataset
Teacher disagreement score0.032
Threshold uncertainty score0.072

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.006
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0010.002
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0020.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0210.012

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 head0.303
Teacher spread0.291 · 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 designNot applicable
Domainnot available
GenreDataset

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

Citations0
Published2021
Admission routes1
Has abstractyes

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