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Record W6911092466 · doi:10.5061/dryad.t4b8gtj8c

Individual behaviour, growth, survival and vulnerability to hunting in a large mammal

2024· dataset· en· W6911092466 on OpenAlexaff

Bibliographic record

VenueDRYAD · 2024
Typedataset
Languageen
Field
Topic
Canadian institutionsUniversité de Sherbrooke
Fundersnot available
KeywordsSelection (genetic algorithm)Context (archaeology)MammalFrench hornPopulationVulnerability (computing)Population growthLife history

Abstract

fetched live from OpenAlex

Humans have exploited wild animals for thousands of years. Recent studies indicate that harvest-induced selection on life-history and morphological traits may lead to ecological and evolutionary changes. Less attention has been given to harvest-induced selection on behavioural traits, especially in terrestrial systems. We assessed in a wild population of large terrestrial mammals whether decades of hunting led to harvest-induced selection on trappability, a proxy of risk-taking behaviour. We investigated links between trappability, horn growth and survival across individuals in early life and quantified the correlations between early life trappability and horn growth with availability to hunters and probability of being shot. We found positive among-individual correlations between early life trappability and horn growth, early life trappability and survival, and early life horn growth and survival. Faster growing individuals were more likely to be available to hunters and shot at a young age. We found no correlations between early life trappability and availability to hunters or probability of being shot. Our results show that correlations between behaviour and growth can occur in wild terrestrial population but may be context dependent. This result highlights the difficulty in formulating general predictions about harvest-induced selection on behaviour, which can be affected by species ecology, harvesting regulations, and harvesting methods used. Future studies should investigate mechanisms linking physiological, behavioural, and morphological traits and how this effects harvest vulnerability to evaluate the potential for harvest to drive selection on behaviour in wild animal populations.

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)0.004
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Dataset · Consensus signal: Dataset
Teacher disagreement score0.146
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.002
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0000.014

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 head0.025
GPT teacher head0.307
Teacher spread0.282 · 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.

Study designNot applicable
Domainnot available
GenreDataset

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 routes1
Has abstractyes

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