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Record W4377047505 · doi:10.3168/jds.2022-23176

Effects of dairy cows' personality traits on their adaptation to an automated milking system following parturition

2023· article· en· W4377047505 on OpenAlexaff
J.E. Brasier, A.J. Schwanke, T.J. DeVries

Bibliographic record

VenueJournal of Dairy Science · 2023
Typearticle
Languageen
FieldVeterinary
TopicAnimal Behavior and Welfare Studies
Canadian institutionsUniversity of Guelph
Fundersnot available
KeywordsIce calvingMilkingDairy cattlePersonalityAnimal scienceLactationBig Five personality traitsAnalysis of varianceBiologyPregnancyPsychologyMathematicsStatisticsSocial psychology

Abstract

fetched live from OpenAlex

The objectives of this study were to determine how dairy cow personality traits affect their adaptation to an automated milking system (AMS) upon parturition, as well as whether these personality traits are consistent across the transition from gestation to lactation. Sixty Holstein dairy cows (19 primiparous and 41 multiparous) were assessed for personality traits using a combined arena test conducted at 24 d before parturition and 24 d after first introduction to an AMS, which occurred ∼3 d after parturition. The combined arena test comprised 3 parts: a novel arena test, a novel object test, and a novel human test. Principal component analysis of the behaviors recorded during the personality assessment revealed 3 factors interpreted as personality traits (75% cumulative variance) in the pre-calving test, interpreted as explore, active, and bold. The post-calving test revealed 2 factors (78% cumulative variance), interpreted as active and explore. Data from d 1 to 7 after introduction to the AMS were summarized by cow and associated with the pre-calving factors, and data from d 21 to 27 after introduction to the AMS were summarized by cow and associated with the post-calving factors. The active trait had a moderate positive correlation between the pre- and post-calving tests, whereas exploration had a weak positive correlation between tests. Cows that scored high for activeness in the pre-calving test tended to have fewer fetching events and a higher coefficient of variation of milk yield in the first 7 d after introduction to the AMS, whereas bolder cows tended to have higher milk yield during that period. In the post-calving test, more active cows tended to have more frequent milkings and voluntary visits per day, as well as a lower cumulative milk yield from d 21 to 27 after introduction to the AMS. Overall, these results indicate that personality traits of dairy cows are associated with adaptation and performance in an AMS, and that personality traits are consistent across the transition period. Specifically, cows that scored high for boldness and activeness adapted better to the AMS immediately after calving, whereas cows that scored low for activeness and high for boldness performed better in terms of milk yield and milking activity in early lactation. This study demonstrates that personality traits affect milking activity and milk yield of dairy cows milked with an AMS and, therefore, may be useful for selection of cows who might best adapt to and use an AMS.

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)0.000
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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
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)0.0010.000

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 head0.069
GPT teacher head0.352
Teacher spread0.283 · 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 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

Citations23
Published2023
Admission routes1
Has abstractyes

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