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Record W3210236058 · doi:10.3390/ani11113031

Postpartum Stressors Cause a Reduction in Mechanical Brush Use in Dairy Cows

2021· article· en· W3210236058 on OpenAlexafffund
Benjamin Lecorps, Allison Welk, Daniel M. Weary, M.A.G. von Keyserlingk

Bibliographic record

VenueAnimals · 2021
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicAgriculture and Farm Safety
Canadian institutionsUniversity of British Columbia
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsBrushStressorAnimal scienceReduction (mathematics)MedicineBiologyEngineeringMathematicsPsychiatryMechanical engineering

Abstract

fetched live from OpenAlex

Dairy cows are often subjected to multiple post-partum stressors but how these stressors impact cows' affective states remain poorly understood. Negative affective states are often associated with reduced expression of low-resilience behaviors, so we explored whether cows would reduce their use of a brush after calving. Before calving, cows were offered the opportunity to use a mechanical brush once a week for 10 min. In Experiment 1, we explored whether cows reduced their use of a mechanical brush after parturition (compared to prepartum values) when subjected to the myriad of stressors typically experienced by cows at this time. In Experiment 2, we assessed the effect of cow-calf separation. Results from Experiment 1 showed that cows displayed a reduced brush use following parturition compared to the week before calving. In Experiment 2, we showed that cows given more time to bond with their calf, and who were separated more recently from their calf, showed a more pronounced reduction in brush use. Cows provided part-time contact with their calf for 29 days also reduced their brush use when they were permanently separated from their calf on day 30 after calving. These results suggest that cows experienced anhedonia and point to new directions for research on dairy cow affective states.

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)0.000
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.934
Threshold uncertainty score0.839

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.038
GPT teacher head0.245
Teacher spread0.207 · 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

Citations17
Published2021
Admission routes2
Has abstractyes

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