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Record W2972386701 · doi:10.1139/cjas-2019-0031

Key animal welfare issues in commercially raised dairy calves: social environment, nutrition, and painful procedures

2019· article· en· W2972386701 on OpenAlexaffvenue
J.H.C. Costa, M.C. Cantor, Nicola A. Adderley, Heather W. Neave

Bibliographic record

VenueCanadian Journal of Animal Science · 2019
Typearticle
Languageen
FieldVeterinary
TopicAnimal Behavior and Welfare Studies
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsAnimal welfareWelfareWeaningBusinessEnvironmental healthPsychologyMedicinePublic economicsEconomicsBiologyEcologyMarket economy

Abstract

fetched live from OpenAlex

Dairy calf welfare concerns are growing and new evidence suggests that the early life environment influences appropriate physical, behavioral, and cognitive development lasting into adulthood. This review highlights key evidence for the impacts of housing, diets, and painful procedures on calf welfare. We argue that these topics are currently critical welfare concerns, but are not the only points of concern. In addition to environmental requirements to maintain optimal health, dairy calves experience other challenges including social and nutritional restrictions. Individual housing is associated with impaired behavioral development and cognitive ability. Pair and group housing can mitigate some of these negative effects and should be encouraged. Restrictive milk allowances (<15% of body weight) lead to poor growth and hunger; these welfare concerns can be addressed with proper enhanced milk allowances and gradual weaning programs. Finally, dehorning is a critical animal welfare issue when pain control is withheld; calves show negative behavioral, physiological, and emotional responses during and after dehorning. The combined use of local anaesthetics and analgesics can mitigate these effects. An industry shift toward providing social companionship, enhanced milk allowances, and pain control during painful procedures would help to improve the welfare of dairy calves in intensive commercial rearing facilities.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.317
Threshold uncertainty score0.682

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 studies0.0010.001
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 head0.032
GPT teacher head0.303
Teacher spread0.271 · 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

Citations47
Published2019
Admission routes2
Has abstractyes

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