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Record W2031194214 · doi:10.1139/z05-059

Between-population differences in nestling size and hematocrit level in blue tits (<i>Parus caeruleus</i>): a cross-fostering test for genetic and environmental effects

2005· article· en· W2031194214 on OpenAlexafffundvenue
Aurélie Simon, D. W. Thomas, Patrice Bourgault, Jacques Blondel, Philippe Perret, Marcel M. Lambrechts

Bibliographic record

VenueCanadian Journal of Zoology · 2005
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicAnimal Behavior and Reproduction
Canadian institutionsUniversité de Sherbrooke
FundersNatural Sciences and Engineering Research Council of CanadaCentre National de la Recherche ScientifiqueEuropean CommissionNational Geographic Society
KeywordsBiologyParusHematocritPopulationEcologyZoologyAbundance (ecology)Demography

Abstract

fetched live from OpenAlex

Geographically separated populations may diverge genetically in response to differing environmental conditions. Two populations of blue tits (Parus caeruleus L., 1758) that inhabit distinct valleys in northern Corsica are exposed to extreme differences in food abundance and parasite loads and show differences in nestling mass and hematocrit levels at fledging. We used partial cross-fostering coupled with experimental manipulation of parasite loads to test the hypothesis that between-population differences in nestling mass and hematocrit reflect adaptive genetic responses to differing parasite prevalence. Although asymptotic mass and hematocrit were strongly affected by variation in parasite loads and caterpillar abundance, we did not detect any significant genetic (population of origin) effect or genotype–environment interaction. We conclude that in these populations of blue tits, asymptotic mass and hematocrit are phenotypically plastic traits that are primarily set by environmental conditions during the sensitive growth phase.

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 score0.071
Threshold uncertainty score0.943

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 head0.034
GPT teacher head0.236
Teacher spread0.203 · 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

Citations37
Published2005
Admission routes3
Has abstractyes

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