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Case study

2019· article· en· W4324264299 on OpenAlexafffundabout
S.J. LeBlanc

Bibliographic record

VenueThe Bovine Practitioner · 2019
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicAgricultural Economics and Policy
Canadian institutionsUniversity of Guelph
FundersHealth CanadaUniversity of MinnesotaPfizer
KeywordsHerdInseminationArtificial inseminationAnimal sciencePregnancyReproductionConfidence intervalMedicinePregnancy rateBiologyInternal medicine

Abstract

fetched live from OpenAlex

The objective of this field study was to measure the effect of implementing a simple systematic reproduction management program in dairy herds with reproductive performance near or below typical in the industry. Thirty-nine herds across Canada that had annual herd 21-day pregnancy rates (PR) between 8 and 15%, and did not have a systematic reproduction program, were enrolled in a program that consisted of: enrollment of cows that were not inseminated by 70 days-in-milk into a timed insemination program (Ovsynch); enrollment of cows diagnosed not pregnant on Ovsynch; and a change to biweekly veterinary visits for reproductive management. Annual herd PR was compared one year after implementation of the program. On average, the systematic management program was associated with a mean increase in PR of 3.6 percentage points (95% confidence interval, 2.5 to 4. 7; P<0.0001), accounting for initial insemination and conception rates, herd size and method of pregnancy diagnosis. Assuming a milk price of $23/cwt to reflect Canadian milk price net of purchase of quota, economic analyses indicated that herds that increased PR by at least two points were estimated to have an annual net profit improvement between $20 and $250 CDN per cow, depending on the initial PR and the magnitude of the increase in PR. Overall, 72 to 77% of herds that implemented the program were estimated to have a net economic benefit.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.485
Threshold uncertainty score0.999

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)0.0020.001

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 head0.025
GPT teacher head0.238
Teacher spread0.214 · 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; both teacher heads agree on what is shown here.

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

Citations0
Published2019
Admission routes3
Has abstractyes

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