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Record W4387662569 · doi:10.1016/j.beproc.2023.104957

How outing conditions relate to the motivation of movement-restricted cattle to access an outdoor exercise yard

2023· article· en· W4387662569 on OpenAlexafffund
Nadège Aigueperse, Véronique Boyer, E. Vasseur

Bibliographic record

VenueBehavioural Processes · 2023
Typearticle
Languageen
FieldVeterinary
TopicAnimal Behavior and Welfare Studies
Canadian institutionsMcGill University
FundersNovalaitDairy Farmers of CanadaNatural Sciences and Engineering Research Council of CanadaMcGill University
KeywordsYardPsychologyHorseback ridingTRIPS architectureMedicinePhysical therapyTransport engineeringEngineering

Abstract

fetched live from OpenAlex

Assessing animal motivation to access a given resource is one method available to evaluate what to provide in the living environments of captive animals. Providing increased opportunities for movement can be seen as an important source of enrichment, but we need to know the point of view of the animal. The objective of our study was to test a novel combination of behaviours in order to assess the motivation of cows to access an outdoor exercise paddock. Three trials were conducted, each enrolling 15-16 tie-stall-housed cows as a model for movement-restricted animals. Cows were provided with access to an outdoor exercise yard 5 days/week for the duration of the trial, each trial presenting different conditions such as paddock size, duration of access and animal handling. We recorded the trips' durations and cows' behaviours during the trips going to (go-out) and coming back (go-in) from the paddock. LMr comparisons on PCA were used to assess cow motivation profiles. The same two dimensions of speed and stop quality emerged from the PCA in all three trials, showing the method's robustness. Additionally, three motivation profiles were established, representing how the cows' motivation was affected by the conditions prevailing in each trial.

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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.055
Threshold uncertainty score0.634

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 communication0.0000.001
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 head0.142
GPT teacher head0.376
Teacher spread0.234 · 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

Citations4
Published2023
Admission routes2
Has abstractyes

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