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Record W4390747347 · doi:10.1111/jzo.13147

Adaptive divergence of seasonal heart plasticity between Canadian and Spanish pumpkinseed sunfish populations

2024· article· en· W4390747347 on OpenAlexafffundabout
S. M. Procopio, Brett M. Studden, Caleb J. Axelrod, Frédéric Laberge, Beren W. Robinson

Bibliographic record

VenueJournal of Zoology · 2024
Typearticle
Languageen
FieldEnvironmental Science
TopicPhysiological and biochemical adaptations
Canadian institutionsUniversity of Guelph
FundersDirectorate for Biological SciencesTrent UniversityUniversity of Guelph
KeywordsSeasonalityLepomisBiologyPhenotypic plasticityAcclimatizationVentricleEcologyForagingZoologyInternal medicinePredation

Abstract

fetched live from OpenAlex

Abstract Laboratory experiments suggest that reversible changes in the heart ventricle phenotype of fish accompany acclimation to temperature change to maintain cardiac function, but related work in fish living in natural conditions is scant. We investigated seasonal variation in heart ventricular mass and collagen content in pumpkinseed sunfish ( Lepomis gibbosus ) living in outdoor ponds where they experienced high seasonality conditions. Additionally, we compared populations adapted to high and low seasonality to evaluate potential divergence in seasonal heart plasticity. Heart ventricular mass decreased in the summer compared to colder seasons only in populations adapted to high seasonality. The absence of seasonal variation in ventricular mass in sunfish adapted to low seasonality was not due to changes in foraging activity, suggesting a loss of ventricle size plasticity due to either costs of plasticity or relaxed selection. Seasonal variation in ventricle collagen content also occurred, with the highest collagen content in summer regardless of population adaptation to high or low seasonality. Only the proportion of thick collagen fibres changed across seasons. We conclude that natural seasonal cues induce plastic responses in some functional heart traits and propose that these responses can rapidly diverge among populations under different seasonal regimes.

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 score0.095
Threshold uncertainty score0.953

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 head0.033
GPT teacher head0.249
Teacher spread0.215 · 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

Citations0
Published2024
Admission routes3
Has abstractyes

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