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Record W2300316369 · doi:10.1890/15-1485.1

Variation in the age of first reproduction: different strategies or individual quality?

2016· article· en· W2300316369 on OpenAlexfundno aff
Rémi Fay, Christophe Barbraud, Karine Delord, Henri Weimerskirch

Bibliographic record

VenueEcology · 2016
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicGenetic and phenotypic traits in livestock
Canadian institutionsnot available
FundersInstitut écologie et environnementSeventh Framework ProgrammeEuropean Research CouncilTerres Australes et Antarctiques FrançaisesInstitut Polaire Français Paul Emile VictorCentre National de la Recherche ScientifiquePolar Knowledge Canada
KeywordsVariation (astronomy)ReproductionEcologyQuality (philosophy)BiologyGeography

Abstract

fetched live from OpenAlex

Although age at first reproduction is a key demographic parameter that is probably under high selective pressure, it is highly variable and the cause of this variability is not well understood. Two non-exclusive hypotheses may explain such variability. It could be the expression of different individual strategies, i.e., different allocation strategies in fitness components, or the consequences of individual difference in intrinsic quality, i.e., some individuals always doing better than others in all fitness components. We tested these hypotheses in the Wandering Albatross investigating relationships between the age at first reproduction and subsequent adult demographic traits. Using finite mixture capture recapture modeling, we demonstrate that the age at first reproduction is negatively related to both reproductive performances and adult survival, suggesting that individual quality was an important factor explaining variation in the age at first reproduction. Our results suggest that age at first breeding is a good predictor of quality in this long-lived seabird species.

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 score0.584
Threshold uncertainty score0.143

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 head0.029
GPT teacher head0.278
Teacher spread0.249 · 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

Citations52
Published2016
Admission routes1
Has abstractyes

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