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Record W3175577797 · doi:10.21423/aabppro20163415

Stakeholder views, including the public, on expectations for dairy cattle welfare

2016· article· en· W3175577797 on OpenAlexafffund
M.A.G. von Keyserlingk, Daniel M. Weary

Bibliographic record

VenueAmerican Association of Bovine Practitioners Conference Proceedings · 2016
Typearticle
Languageen
FieldVeterinary
TopicAnimal Behavior and Welfare Studies
Canadian institutionsUniversity of British Columbia
FundersNatural Sciences and Engineering Research Council of CanadaZoetisDairy Farmers of Canada
KeywordsAnimal welfareAgricultureWelfareDairy industryBusinessWork (physics)Dairy cattleStakeholderDairy farmingHarmony (color)MarketingBest practicePublic relationsPolitical scienceEconomicsMarket economyEngineeringGeographyManagement

Abstract

fetched live from OpenAlex

Animal welfare is emerging as one of the key social concerns regarding animal agriculture. Concern for the welfare of farms animals is not new, but the last few years have seen increased interest in farm practices. One of the dairy industry's core strengths is the very positive view that many people have about dairy farming. Many consumers believe that cows spend their days grazing green pastures. This strength can also be regarded as a threat if some industry practices no longer match evolving public expectations. Every year there are fewer farms, and the ever decreasing proportion of society that works within this industry will never be able to able to 'educate' the large majority, at least not on all issues, all of the time. Moreover, the farmers themselves are part of this rapidly evolving society, and practices that were accepted by past generations as necessary may not seem so to the next generation of producers. Change will happen. During my presentation I will highlight some of our most recent work on engaging dairy farmers and the public as a means to help identify practices that do and do not come into harmony with public expectations.

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)0.002
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 score0.346
Threshold uncertainty score0.528

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
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 head0.136
GPT teacher head0.357
Teacher spread0.221 · 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

Citations2
Published2016
Admission routes2
Has abstractyes

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