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Record W3118651968 · doi:10.21423/aabppro20197247

“If it ain’t broke don’t fix it”

2019· article· en· W3118651968 on OpenAlexaffabout
E. Belage, S. Croyle, Andria Jones‐Bitton, Simon Dufour, D.F. Kelton

Bibliographic record

VenueAmerican Association of Bovine Practitioners Conference Proceedings · 2019
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicMilk Quality and Mastitis in Dairy Cows
Canadian institutionsUniversité de MontréalUniversity of Guelph
Fundersnot available
KeywordsMilkingMastitisBusinessDairy industryHygieneQuality (philosophy)Agricultural scienceAnimal scienceMedicineFood scienceEnvironmental scienceBiology

Abstract

fetched live from OpenAlex

A large part of preventing mastitis on dairy farms is achieved through the use of recommended milking practices (RMPs) that have been established and promoted by industry and milk quality organizations such as the National Mastitis Council and the Canadian Bovine Mastitis and Milk Quality Research Network. These practices include use of milking gloves, foremilk stripping, use of pre and post milking teat disinfectants, drying teats prior to milking, and use of automatic takeoffs on milking machines. However, many producers do not adopt these measures, or they only do so partially. This study aimed to explore the attitudes and perceptions of Ontario dairy farmers towards barriers for implementation of RMPs, and to investigate what motivates behavior change in relation to milking hygiene.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.543
Threshold uncertainty score0.997

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0040.000

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 head0.015
GPT teacher head0.249
Teacher spread0.233 · 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.

Study designNot applicable
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
Published2019
Admission routes2
Has abstractyes

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