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Record W4226251493 · doi:10.1016/j.crphys.2022.03.003

A sudden change of heart: Warm acclimation can produce a rapid adjustment of maximum heart rate and cardiac thermal sensitivity in rainbow trout

2022· article· en· W4226251493 on OpenAlexafffund
Matthew J. H. Gilbert, Olivia A. Adams, Anthony P. Farrell

Bibliographic record

VenueCurrent Research in Physiology · 2022
Typearticle
Languageen
FieldEnvironmental Science
TopicPhysiological and biochemical adaptations
Canadian institutionsUniversity of British Columbia
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsAcclimatizationRainbow troutCritical thermal maximumHeart rateBiologyFish <Actinopterygii>Environmental scienceEcologyAnimal scienceFisheryEndocrinologyBlood pressure

Abstract

fetched live from OpenAlex

Warm acclimation in fish is often characterized by an increase in heat tolerance and a reduction in physiological rates to improve the scope to respond to additional challenges including further warming. The speed of these responses can determine their effectiveness. However, acclimation rates vary across levels of biological organization and are poorly understood in part because most research is conducted after an acclimation period of >3 weeks, when acclimation is presumed to be complete. Here we show that when rainbow trout were transferred from 10 to 18 °C, over 50% of the total reduction of maximum heart rate (ƒHmax) (i.e. the thermal compensation at moderate temperatures) occurred within 72 h, with further compensation occurring more gradually over the following 25 days. Also, the ability to increase ƒHmax with acute warming improved within 24 h resulting in a 30% rise in peak ƒHmax, but this ultimately declined again with prolonged (28 days) exposure to 18 °C. In contrast with some previous studies, upper critical temperatures for ƒHmax did not increase. Nonetheless, we demonstrate that rapid cardiac plasticity is possible in rainbow trout and likely blunts the impacts of thermal variation over relatively short timescales, such as that associated with heat waves and migration between water bodies.

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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.953
Threshold uncertainty score0.306

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.001
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 head0.119
GPT teacher head0.349
Teacher spread0.230 · 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 designBench or experimental
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

Citations13
Published2022
Admission routes2
Has abstractyes

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