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Record W2782380249 · doi:10.1093/conphys/cox076

Metabolic rates of embryos and alevin from a cold-adapted salmonid differ with temperature, population and family of origin: implications for coping with climate change

2017· article· en· W2782380249 on OpenAlexafffund
Catharine J Cook, Gary Burness, Chris C. Wilson

Bibliographic record

VenueConservation Physiology · 2017
Typearticle
Languageen
FieldEnvironmental Science
TopicPhysiological and biochemical adaptations
Canadian institutionsMinistry of Natural Resources and ForestryTrent University
FundersRocky Mountain Research StationOntario Ministry of Natural Resources and Forestry
KeywordsBiologyCoping (psychology)Cold climateClimate changePopulationCold stressEcologyDemographyGeneticsPsychology

Abstract

fetched live from OpenAlex

) are at higher risk of mortality with increasing water temperatures. To determine the amount of variation present in early life, which may allow for potential adaptation to increasing temperature, we examined the routine metabolic rates (RMR) of wild-origin brook trout embryos and alevins reared at normal (5°C) and elevated (9°C) temperatures. The experiment was structured to examine variation in RMR within and among several levels of biological organization (family, population and ancestral type (native vs. mixed ancestry)). As expected, family and temperature variables were most important for predicting RMR and body mass, although population-level differences also existed when family was excluded for more detailed analysis. Additionally, body mass strongly influenced RMR at all life stages except for eyed embryos. When family identity was removed from the analysis, population became the most significant variable. Variation in RMR and mass within and among populations may indicate existing adaptive potential within and among brook trout populations to respond to predicted warming under climate change scenarios.

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 score0.795
Threshold uncertainty score0.260

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 head0.054
GPT teacher head0.280
Teacher spread0.225 · 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

Citations18
Published2017
Admission routes2
Has abstractyes

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