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Record W4410392101 · doi:10.1139/facets-2023-0241

Determining the upper thermal tolerance of Athabasca Rainbow Trout (<i>Oncorhynchus mykiss</i>) across naturally varying stream temperatures

2025· article· en· W4410392101 on OpenAlexafffundvenueabout
Sarah Hnytka, Jordan S. Rosenfeld, Eva C. Enders

Bibliographic record

VenueFACETS · 2025
Typearticle
Languageen
FieldEnvironmental Science
TopicFish Ecology and Management Studies
Canadian institutionsMinistry of EnvironmentFisheries and Oceans Canada
FundersFisheries and Oceans Canada
KeywordsRainbow troutRainbowEnvironmental scienceOncorhynchusFisheryBiologyFish <Actinopterygii>Physics

Abstract

fetched live from OpenAlex

Stream temperature is a key driver of physiological function in ectothermic fish, and fish have clear upper and lower limits to thermal habitat use. Stream temperature increases from climate change are a major threat to coldwater taxa like Athabasca Rainbow Trout ( Oncorhynchus mykiss), listed as Endangered under Canada's Species at Risk Act. To better understand their vulnerability to climate change and prioritize recovery locations, Athabasca Rainbow Trout were collected in August 2021 from three streams in the upper Athabasca River, Alberta, Canada, across a gradient of thermal regimes ranging from cold to warm. Individual thermal tolerance was measured using two physiological metrics: agitation temperature ( T ag ) and critical thermal maxima (CT max ) using a portable streamside laboratory. T ag is a behavioural indicator of a thermal avoidance threshold, whereas CT max is a physiological response that can be interpreted as the upper thermal limit where long term survival is reduced. Results provide strong evidence that CT max is a plastic metric in Athabasca Rainbow Trout; however, T ag is not. This suggests that Athabasca Rainbow Trout demonstrate some thermal plasticity in terms of CT max , yet the lack of plasticity in T ag indicates potential limitations in their adaptability to warming stream temperatures.

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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.028
Threshold uncertainty score0.452

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 studies0.0010.000
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 head0.006
GPT teacher head0.245
Teacher spread0.239 · 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

Citations1
Published2025
Admission routes4
Has abstractyes

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