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Record W2933857058 · doi:10.1038/s41598-019-41798-7

Calf aversion to hot-iron disbudding

2019· article· en· W2933857058 on OpenAlexafffund
Thomas Ede, Benjamin Lecorps, M.A.G. von Keyserlingk, Daniel M. Weary

Bibliographic record

VenueScientific Reports · 2019
Typearticle
Languageen
FieldVeterinary
TopicAnimal Behavior and Welfare Studies
Canadian institutionsUniversity of British Columbia
FundersNatural Sciences and Engineering Research Council of CanadaGovernment of Canada
KeywordsCauterizationMedicineAnesthesiaSurgery

Abstract

fetched live from OpenAlex

Dairy calves are routinely disbudded by cauterization with a hot iron. To mitigate the intra-operative and initial post-operative pain associated with this procedure some farmers provide calves general and local anesthetics, but it is unknown if the procedure remains aversive. We used a place-conditioning paradigm to assess aversion caused by hot-iron cautery with a local anesthetic compared to a sham procedure. A test area was divided into three equally sized pens: two 'treatment' pens with distinct visual cues were connected by a central 'neutral' pen. Each calf went through the disbudding procedure and a 6-h recovery period in one treatment pen and the control procedure in the other treatment pen. In three tests (48, 72 and 96 h after the second treatment), calves could freely roam among the pens until they chose to lie down, ending the session. Calves spent less time in either of the treatment pens compared to the central pen. When only comparing the two treatment pen, calves spent less time in the disbudding pen, especially during the first test. Calves were also less likely to lie down in the pen associated with the disbudding procedure. We conclude that even with the use of a local anesthetic, hot-iron disbudding is salient and aversive for calves, indicating the need to refine or avoid the procedure.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.674
Threshold uncertainty score1.000

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 studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.002

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 head0.034
GPT teacher head0.314
Teacher spread0.280 · 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; both teacher heads agree on what is shown here.

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

Citations36
Published2019
Admission routes2
Has abstractyes

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