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Record W4408949865 · doi:10.1111/jfb.70031

Acclimation conditions and subsequent acute hypoxia challenges do not affect the frequency of erythrocyte nuclear segmentation in triploid brook charr <i>Salvelinus fontinalis</i> (<scp>Mitchill</scp> 1814)

2025· article· en· W4408949865 on OpenAlexafffund
John Clark, Christopher A. Baker, Sarah A. McGeachy, Rebecca R. Jensen, Tillmann J. Benfey

Bibliographic record

VenueJournal of Fish Biology · 2025
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicReproductive biology and impacts on aquatic species
Canadian institutionsUniversity of New Brunswick
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsFontinalisBiologySalvelinusAcclimatizationHypoxia (environmental)ZoologyEcologyFish <Actinopterygii>FisheryTroutOxygen

Abstract

fetched live from OpenAlex

Routine observations of blood smears have repeatedly shown that triploid fish have a greater proportion of erythrocytes with nuclear segmentation (ENS) than diploids; however, there is as yet no understanding of why this is the case and whether it affects erythrocyte function. In an attempt to address this, we examined blood smears from two previous experiments to determine whether ENS frequency is affected by rearing conditions (acclimation temperature or dissolved oxygen level) and subsequent acute aerobic distress in diploid and triploid brook charr Salvelinus fontinalis (Mitchill 1814). As expected, triploids had a higher ENS frequency than corresponding diploids, but with no biologically significant effect of experimental conditions on ENS frequency in triploids. ENS was too rare to examine the effects of these conditions in diploids.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.006

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 head0.016
GPT teacher head0.285
Teacher spread0.269 · 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 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

Citations1
Published2025
Admission routes2
Has abstractyes

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