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Record W2890163949 · doi:10.3168/jds.2018-14919

Hot topic: Management of cull dairy cows—Consensus of an expert consultation in Canada

2018· article· en· W2890163949 on OpenAlexafffundabout
Jane Stojkov, Gary J. Bowers, Michael Draper, T.F. Duffield, P. Duivenvoorden, M. Groleau, Deb Haupstein, R. Peters, Jane Pritchard, C. Radom, Nicole Sillett, W. Skippon, H. Trépanier, David Fraser

Bibliographic record

VenueJournal of Dairy Science · 2018
Typearticle
Languageen
FieldVeterinary
TopicAnimal Behavior and Welfare Studies
Canadian institutionsMinistère de l'Agriculture, des Pêcheries et de l'AlimentationSaputo (Canada)Ministry of AgricultureGovernment of British ColumbiaUniversity of British ColumbiaUniversity of GuelphMinistry of Agriculture, Food and Rural AffairsCanadian Food Inspection Agency
FundersNatural Sciences and Engineering Research Council of CanadaLoblaw Companies LimitedDairy Farmers of Canada
KeywordsCullingLivestockBusinessEnforcementAnimal welfareAgricultural scienceCommon value auctionHerdMarketingVeterinary medicineMedicineGeographyEconomicsPolitical science

Abstract

fetched live from OpenAlex

Many cull dairy cows enter the marketing system and travel to widely dispersed and specialized slaughter plants, and they may experience multiple handling events (e.g., loading, unloading, mixing), change of ownership among dealers, and feed and water deprivation during transport and at livestock markets. The objectives of this study were to describe the diverse management of cull dairy cows in Canada and establish consensus on ways to achieve improvements. A 2-day expert consultation meeting was convened, involving farmers, veterinarians, regulators, and experts in animal transport, livestock auction, and slaughter. The 15 participants, recruited from across Canada, discussed regional management practices for cull cattle, related risk factors, animal welfare problems, and recommendations. An audio recording of the meeting was used to extract descriptive data on cull cattle management and identify points of agreement. Eight consensus points were reached: (1) to assemble information on travel times and delays from farm to slaughter; (2) to increase awareness among producers and herd veterinarians of potential travel distances and delays; (3) to promote pro-active culling; (4) to improve the ability of personnel to assess animal condition before loading; (5) to identify local options for slaughter of cull dairy cows; (6) to investigate different management options such as emergency slaughter and mobile slaughter; (7) to ensure that all farms and auctions have, or can access, personnel trained and equipped for euthanasia; and (8) to promote cooperation among enforcement agencies and wider adoption of beneficial regulatory options.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.626
Threshold uncertainty score0.980

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.055
GPT teacher head0.345
Teacher spread0.289 · 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

Citations28
Published2018
Admission routes3
Has abstractyes

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