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Record W2215880510 · doi:10.3168/jds.2015-9925

Imagining the ideal dairy farm

2015· article· en· W2215880510 on OpenAlexaff
Clarissa Silva Cardoso, María José Hötzel, Daniel M. Weary, Jesse Robbins, M.A.G. von Keyserlingk

Bibliographic record

VenueJournal of Dairy Science · 2015
Typearticle
Languageen
FieldVeterinary
TopicAnimal Behavior and Welfare Studies
Canadian institutionsUniversity of British Columbia
FundersConselho Nacional de Desenvolvimento Científico e Tecnológico
KeywordsIdeal (ethics)Agricultural scienceBiologyPolitical scienceLaw

Abstract

fetched live from OpenAlex

Practices in agriculture can have negative effects on the environment, rural communities, food safety, and animal welfare. Although disagreements are possible about specific issues and potential solutions, it is widely recognized that public input is needed in the development of socially sustainable agriculture systems. The aim of this study was to assess the views of people not affiliated with the dairy industry on what they perceived to be the ideal dairy farm and their associated reasons. Through an online survey, participants were invited to respond to the following open-ended question: "What do you consider to be an ideal dairy farm and why are these characteristics important to you?" Although participants referenced social, economic, and ecological aspects of dairy farming, animal welfare was the primary issue raised. Concern was expressed directly about the quality of life for the animals, and the indirect effect of animal welfare on milk quality. Thus participants appeared to hold an ethic for dairy farming that included concern for the animal, as well as economic, social, and environmental aspects of the dairy system.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.009
metaresearch head score (Gemma)0.008
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.009
Threshold uncertainty score0.050

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0090.008
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0040.015
Scholarly communication0.0040.004
Open science0.0010.004
Research integrity0.0030.004
Insufficient payload (model declined to judge)0.0020.000

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 head0.103
GPT teacher head0.371
Teacher spread0.268 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
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

Citations164
Published2015
Admission routes1
Has abstractyes

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