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Record W2604735693 · doi:10.1139/facets-2016-0053

Physiological responses to a short-term, environmentally realistic, acute heat stress in Atlantic salmon, <i>Salmo salar</i>

2017· article· en· W2604735693 on OpenAlexafffundvenue
Melanie J. Gallant, Sacha LeBlanc, Tyson J. MacCormack, Suzanne Currie

Bibliographic record

VenueFACETS · 2017
Typearticle
Languageen
FieldEnvironmental Science
TopicPhysiological and biochemical adaptations
Canadian institutionsMount Allison UniversityUniversité de MonctonUniversity of Saskatchewan
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsSalmoShock (circulatory)Heat shock proteinJuvenileHSF1BiologyFish <Actinopterygii>Hsp70ZoologyFisheryEcologyInternal medicineMedicineBiochemistry

Abstract

fetched live from OpenAlex

Atlantic salmon populations are declining, and warming river temperatures in the summer months are thought to be a significant contributing factor. We describe the time course of cellular and metabolic responses to an ecologically relevant short-term thermal cycle in juvenile Atlantic salmon. We then examined whether this heat event would affect tolerance to a subsequent heat shock in terms of critical thermal maximum (CTmax). Fish induced heat shock protein 70 in red blood cells, heart, liver, and red and white muscle; whole blood glucose and lactate transiently increased during the heat cycle. In contrast, we observed no significant effect of a prior heat shock on CTmax. The CTmax was positively correlated with Fulton’s condition factor suggesting that fish with greater energy reserves are more thermally tolerant. Atlantic salmon activate cellular protection pathways in response to a single thermal cycle and appear to cope with this short-term, ∼1 d heat shock, but this challenge may compromise the ability to cope with subsequent heat events.

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)0.000
metaresearch head score (Gemma)0.000
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

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)0.0020.000

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 head0.032
GPT teacher head0.278
Teacher spread0.246 · 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

Citations48
Published2017
Admission routes3
Has abstractyes

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