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Record W2795423180 · doi:10.1038/s41598-017-17214-3

Pessimism and fearfulness in dairy calves

2018· article· en· W2795423180 on OpenAlexafffund
Benjamin Lecorps, Daniel M. Weary, M.A.G. von Keyserlingk

Bibliographic record

VenueScientific Reports · 2018
Typearticle
Languageen
FieldVeterinary
TopicAnimal Behavior and Welfare Studies
Canadian institutionsUniversity of British Columbia
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsPessimismTraitPsychologyPersonalityBig Five personality traitsAmbiguityDevelopmental psychologySocial psychology

Abstract

fetched live from OpenAlex

Animals that experience situations likely to induce negative emotions show changes in judgment associated with pessimism. Few studies have focused on whether animals express stable differences in pessimism and whether these differences are related to personality traits. The first aim of this study was to explore if dairy calves are consistent over time in making judgments under ambiguous situations. Our second aim was to determine whether individual differences in judgment bias are related to conventional personality traits assessed using four standardized tests (Open field, Novel object, Human reactivity and Social motivation test). We subjected animals to two sessions of judgment bias and personality trait tests at 25 and 50 d of age. Individual differences in judgment bias were consistent over time with some animals persistently making more pessimistic choices compared to others. Two main dimensions of personality (Fearfulness and Sociability), obtained through principal component analysis, were also highly consistent over time. Pessimism was related to fearfulness, with more fearful calves making more pessimistic judgments. We conclude that dairy calves differ in the way they perceive and react to ambiguity and that this relates to individual differences in fearfulness.

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)0.001
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 score0.123
Threshold uncertainty score0.362

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
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 head0.054
GPT teacher head0.342
Teacher spread0.288 · 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

Citations73
Published2018
Admission routes2
Has abstractyes

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