Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.044 | 0.012 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".