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Record W2795840631 · doi:10.3168/jds.2017-14320

Use of on-farm emergency slaughter for dairy cows in British Columbia

2018· article· en· W2795840631 on OpenAlexaffabout
Katherine E. Koralesky, David Fraser

Bibliographic record

VenueJournal of Dairy Science · 2018
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicAgriculture and Farm Safety
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsLamenessMedicineStunningDairy cattleIce calvingAnimal welfareAnimal scienceVeterinary medicineSurgeryPregnancyBiologyLactationInternal medicine

Abstract

fetched live from OpenAlex

On-farm emergency slaughter (OFES), whereby inspection, stunning, and bleeding occur on the farm before the carcass is transported to a slaughterhouse, is permitted in some jurisdictions as a means to avoid inhumane transportation while salvaging meat from injured animals. However, OFES is controversial and its use for dairy cows has been little studied. Inspection documents for 812 dairy cows were examined to identify how OFES was used for dairy cows in British Columbia, Canada, over 16.5 mo. Producers used OFES for dairy cows aged 1 to 13 yr (median of 4 yr). Leg, hip, nerve, spinal, foot, and hind-end injuries or conditions (in that order) were the most common reasons for OFES, and some cases may have been a consequence of calving. Foot conditions were disproportionately common among cows 5 yr and older, and hind-end conditions were disproportionately common among cows 6 yr and older. Producers used OFES promptly after traumatic injury (within 1 d) for some cows, but OFES was delayed for others, sometimes until cows had been nonambulatory for 2 to 6 d. In some cases, OFES was used for nontraumatic chronic conditions, such as lameness and hind-end weakness, rather than traumatic injuries such as fractures and dislocated hips. Use of OFES appears to conform to the purpose of the program when used promptly after traumatic injuries, but clear guidelines are needed to avoid inappropriate use and delays that may prolong animal suffering.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.672
Threshold uncertainty score0.972

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.031
GPT teacher head0.248
Teacher spread0.217 · 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

Citations16
Published2018
Admission routes2
Has abstractyes

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