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Record W4400678940 · doi:10.51291/2377-7478.1833

Wildlife conservation: The importance of individual personality traits and sentience

2024· article· en· W4400678940 on OpenAlexaff
Karen A Owens, Gosia Bryja, Marc Bekoff

Bibliographic record

VenueAnimal Sentience · 2024
Typearticle
Languageen
FieldPsychology
TopicAnimal and Plant Science Education
Canadian institutionsColumbia College
Fundersnot available
KeywordsSentienceWildlifeWildlife conservationPsychologyBig Five personality traitsNature ConservationWildlife managementPersonalityEcologyGeographySocial psychologyEnvironmental ethicsBiology

Abstract

fetched live from OpenAlex

Individual differences in personality types within the same species have been studied much less than differences between species and populations. Personality differences are related to risk-taking and exploration, which in turn correlate with individuals' daily responses, decisions, and fitness. Bold and shy personality types can have different advantages and disadvantages under different social or environmental pressures. Analyzing personality differences has helped clarify how elk habituate to a well-populated area and how management strategies can be adapted to them. For wolves newly repatriated to Colorado, individual personality factors are likely to prove important for adapting to their new homes as well as to the needs of the people cohabiting them. Animal and human factors need to be investigated jointly for the long-term success of conservation initiatives.

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)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0020.001
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.055
GPT teacher head0.326
Teacher spread0.271 · 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 designTheoretical or conceptual
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

Citations15
Published2024
Admission routes1
Has abstractyes

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