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Record W2032936960 · doi:10.1080/01652176.2013.799791

Herd level approach to high bulk milk somatic cell count problems in dairy cattle

2013· review· en· W2032936960 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueVeterinary Quarterly · 2013
Typereview
Languageen
FieldAgricultural and Biological Sciences
TopicMilk Quality and Mastitis in Dairy Cows
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsUdderSomatic cell countHerdMastitisPaymentWork (physics)Common value auctionBusinessMedicineMicroeconomicsEconomicsLactationVeterinary medicineFinanceBiologyEngineering

Abstract

fetched live from OpenAlex

Since the introduction of the standard mastitis prevention program in the late 1960s, enormous progress has been made in decreasing the average bulk milk somatic cell count (BMSCC). In many countries, reduction of BMSCC has been encouraged through premium payments or penalty systems. However, the success of the program depends heavily on consistent implementation of management practices. The approach to problem solving in a herd with high BMSCC must include the following elements: (1) problem definition using primary udder health parameters; (2) detection of cows causing the problem; (3) definition of short- and long-term goals; (4) formulation and implementation of a herd management plan; and (5) evaluation of the results. Findings and plans are recorded for use at follow-up visits. Every high BMSCC problem can be solved if farmers are sufficiently motivated, if farm advisors are sufficiently knowledgeable, and if farmer and advisors work together according to a jointly determined plan.

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.

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 categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.990
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
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)0.0000.001

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 head0.138
GPT teacher head0.289
Teacher spread0.151 · 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