Evaluation of the Dairy Comp 305 Module “Cow Value” in Two Ontario Dairy Herds
Bibliographic record
Abstract
The study was conducted to evaluate how the "Cow Value" module of Dairy Comp 305 (Valley Agricultural Software, Tulare, CA) performed under commercial conditions. The "Cow Value" module, COWVAL, computes a farm-specific net present value relative to an average replacement heifer for each cow in the milking and dry herd, which allows a ranking of the cows on the farm compared with replacing her with a typical replacement heifer on that farm. The average replacement heifer is used as the baseline for comparison and has a COWVAL of $0. Retaining a cow with a negative COWVAL is projected to be less profitable than replacing that cow with a new heifer. The objectives of the study were to explore trends in COWVAL over and during multiple lactations for the same cows; to describe factors that influence changes in COWVAL from one monthly Dairy Herd Improvement test to the next; and to evaluate the behavior of COWVAL after it drops below a baseline of $0 during the lifetime of a cow. Monthly Dairy Comp 305 backup cow files from 2 On-tario dairy herds between December 1999 and Decem-ber 2005 were used to generate COWVAL and list production, reproduction, and disease data for the milking cows. In total, 1,463 cows and 20,071 tests were analyzed. Within the first 60 d in milk (DIM), COWVAL was unstable and showed large fluctuations over a range of several thousand Canadian dollars (Can$). After 60 DIM COWVAL was relatively stable. The variability from month to month became less as the lactation progressed and the risk of a change in reproductive status decreased. The reproductive status of the cow influ-enced COWVAL: fresh, open, and pregnant cows had a greater COWVAL than cows declared "do not breed." As parity increased, there was a tendency toward lower COWVAL and smaller monthly changes in COWVAL. The COWVAL of 170 cows dropped below the baseline of $0 after 60 DIM. The COWVAL of 54% of those cows remained below $0, whereas 31.6% had a subsequent COWVAL > $500 (Can$). Farm management should not rely exclusively on COWVAL for culling decisions, particularly for cows that have not had at least 3 milk tests.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
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".