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Record W2799471077 · doi:10.1093/conphys/coy023

Impacts of environmental matching on the routine metabolic rate and mass of native and mixed-ancestry brook trout (Salvelinus fontinalis) fry

2018· article· en· W2799471077 on OpenAlexafffund
Catharine J Cook, Chris C. Wilson, Gary Burness

Bibliographic record

VenueConservation Physiology · 2018
Typearticle
Languageen
FieldEnvironmental Science
TopicPhysiological and biochemical adaptations
Canadian institutionsMinistry of Natural Resources and ForestryTrent University
FundersNatural Sciences and Engineering Research Council of CanadaOntario Ministry of Natural Resources and Forestry
KeywordsSalvelinusFontinalisTroutBiologyFisheryMatching (statistics)EcologyZoologyFish <Actinopterygii>Statistics

Abstract

fetched live from OpenAlex

) affected mass and routine metabolic rate (RMR) of a subsequent life stage (free-swimming fry). As part of this, we assessed the contributions and importance of hierarchical levels of biological organization [ancestral type (native vs. hatchery-introgressed), population, and family] to variability in mass and RMR of fry. As embryos and alevin, individuals were reared at either natural environmental (5°C) or elevated (9°C) temperatures and then acclimated to either matched or mismatched temperature treatments once yolk sacs were resorbed. Mass differences among fry were strongly influenced by population of origin as well as initial rearing and final acclimation temperatures. Variation in mass-adjusted RMR of fry was also strongly accounted for by source population, acclimation temperature, and individual mass. A significant interaction between population RMR and final acclimation temperature indicated that not all brook trout populations responded the same way to temperature changes. In contrast to expectations, the highest ancestry category (native vs. introgressed) did not significantly influence mass or mass-adjusted RMR.

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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.729
Threshold uncertainty score0.375

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 studies0.0000.001
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 head0.021
GPT teacher head0.239
Teacher spread0.218 · 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

Citations15
Published2018
Admission routes2
Has abstractyes

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