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Record W3051616379 · doi:10.3390/ani10081440

Individual Variability in Response to Social Stress in Dairy Heifers

2020· article· en· W3051616379 on OpenAlexafffund
Emeline Nogues, Benjamin Lecorps, Daniel M. Weary, M.A.G. von Keyserlingk

Bibliographic record

VenueAnimals · 2020
Typearticle
Languageen
FieldVeterinary
TopicAnimal Behavior and Welfare Studies
Canadian institutionsUniversity of British Columbia
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsAgonistic behaviourAggressionTime budgetCoping (psychology)PsychologySynchronization (alternating current)Animal scienceDevelopmental psychologyBiologyEcologyClinical psychologyMathematics

Abstract

fetched live from OpenAlex

Regrouping is associated with increased aggression, and disruption of time-budgets. Individuals vary in how well they cope with social stress. Our objective was to describe individual differences in agonistic behavior in dairy heifers after regrouping, and determine how time-budget and behavioral synchronization were affected by these coping strategies. A total of 30 heifers were individually regrouped at 5-months of age into stable groups of 12 unfamiliar animals. For 24 h, agonistic behaviors initiated and received by the regrouped heifer were continuously recorded, and standing, resting and feeding time and synchronization were sampled every 5 min. Scores of engagement in agonistic interactions and avoidance of interactions were calculated for each regrouped heifer. Linear mixed effects models were used to assess whether these two response types were related, and how variation in these responses related to activity and synchronization. Engaged heifers displayed lower avoidance and spent more time feeding. Avoidant heifers spent less time feeding and resting, and were less synchronized while feeding. We conclude that dairy heifers differ in social coping strategy when regrouped through different levels of engagement and avoidance, and that these differences affected their time-budget and behavioral synchronization.

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)0.001
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 score0.098
Threshold uncertainty score0.682

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
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)0.0000.000

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 head0.111
GPT teacher head0.367
Teacher spread0.255 · 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

Citations26
Published2020
Admission routes2
Has abstractyes

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