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Record W2797876402 · doi:10.1163/1568539x-00003490

Costs and benefits of post-weaning associations in mountain goats

2018· article· en· W2797876402 on OpenAlexaff
Karina Charest Castro, Mathieu Leblond, Steeve D. Côté

Bibliographic record

VenueBehaviour · 2018
Typearticle
Languageen
FieldPsychology
TopicNeuroendocrine regulation and behavior
Canadian institutionsEnvironment and Climate Change CanadaUniversité LavalCenter for Northern Studies
FundersInnovative Research Group Project of the National Natural Science Foundation of China
KeywordsWeaningDemographyReproductive successPopulationBiologyEcologyAnimal science

Abstract

fetched live from OpenAlex

Abstract To better understand the potential costs and benefits of prolonged parental care in gregarious species, we studied post-weaning associations in a marked population of mountain goats (Oreamnos americanus) monitored for 22 years. We calculated the occurrence and frequency of associations involving 1- and 2-year-old juveniles. We investigated (1) the influence of maternal characteristics and population size on the formation of post-weaning associations, (2) the short-term costs of associations on maternal reproductive success, and (3) the short-term benefits of associations on life-history traits of juveniles. We found that barren mothers associated more frequently with 1-year-olds than summer yeld and lactating mothers. Associations with 2-year-olds tended to increase the probability that a mother would be barren the following year. Post-weaning associations did not influence the body mass of newborn kids nor the body mass and survival of juveniles. We discuss how benefits for associated juveniles may appear later in life.

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 score0.001
Threshold uncertainty score0.003

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)0.0010.000

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 head0.342
Teacher spread0.304 · 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

Citations5
Published2018
Admission routes1
Has abstractyes

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