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Record W4256469983 · doi:10.1098/rsbl.2019.0825

Is repeatability of metabolic rate influenced by social separation? A test with a teleost fish

2020· article· en· W4256469983 on OpenAlexaff
Yan Huang, Shi‐Jian Fu, Steven J. Cooke, Jigang Xia

Bibliographic record

VenueBiology Letters · 2020
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicAnimal Behavior and Reproduction
Canadian institutionsCarleton University
FundersNational Natural Science Foundation of China
KeywordsRepeatabilityBiologyMetabolic rateFish <Actinopterygii>Carassius auratusCrucian carpAdaptation (eye)Separation (statistics)EcologyZoologyFisheryStatisticsEndocrinology

Abstract

fetched live from OpenAlex

Metabolic rates are typically thought to have important influences on fitness and more broadly be relevant to the ecology and evolution of animals. Previous studies demonstrate that metabolic rates are repeatable to a certain extent under constant conditions, but how social conditions influence the repeatability of metabolic rate remains largely unknown. In this study, we investigated the repeatability of resting metabolic rate (RMR) in the highly social crucian carp ( Carassius auratus ) after being socially separated for different time periods relative to control fish that were not socially separated. We found that RMR was repeatable in fish in the control group, while the repeatability of RMR disappeared quickly (even within 7 days) when fish were exposed to social separation. This study is the first to our knowledge to examine the role of social separation for different time periods on the repeatability of intra-individual physiological variation in fish. We highlight that the inter-individual repeatability of metabolic rate can be substantial over time but was eliminated by social separation. The findings indicate that the repeatability of metabolic rate in fish is condition dependent, and that the change in repeatability of metabolic rate should not be overlooked when considering the ecological and evolutionary effects of environmental change.

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)0.000
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.651
Threshold uncertainty score0.131

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 head0.022
GPT teacher head0.257
Teacher spread0.235 · 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

Citations7
Published2020
Admission routes1
Has abstractyes

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