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Record W2913337901 · doi:10.1163/1568539x-00003525

Male group members are costly to plurally breeding Octodon degus females

2019· article· en· W2913337901 on OpenAlexaff
Loren D. Hayes, Loreto A. Correa, Sebastián Abades, Cuilan Gao, Luis A. Ebensperger

Bibliographic record

VenueBehaviour · 2019
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicAnimal Behavior and Reproduction
Canadian institutionsToronto Metropolitan University
Fundersnot available
KeywordsBiologyDemographyLitterOffspringKin recognitionPer capitaEcologyPopulation

Abstract

fetched live from OpenAlex

Abstract We report the results of a 6-year study of social (number of adult males/females, relatedness of females, communal litter size) and ecological (mean/CV of food abundance, soil hardness, burrow openings) factors influencing the direct fitness of plurally breeding degu (Octodon degus) females. The best fit models for per capita offspring weaned and standardized variance in direct fitness (within-group variation) included the number of adult males per group. Per capita number of offspring weaned decreased and standardized variance in direct fitness increased with increasing number of adult males per group. Thus, females experience a cost associated with males that is not shared equally. Standardize variance in direct fitness decreased with increasing communal litter size. All other factors were not significant predictors of direct fitness variation. Our study suggests that plural breeding may not be as egalitarian as previously thought. Consequences of plural breeding may be influenced by intra- and inter-sexual conflict.

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)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.002
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
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 head0.038
GPT teacher head0.247
Teacher spread0.208 · 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 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

Citations26
Published2019
Admission routes1
Has abstractyes

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