Maximization of Lactation Milk Production Without Decreasing Persistency
Bibliographic record
Abstract
This study treats each daily estimated breeding value (EBV) of the lactation as a separate trait to modify the lactation curve on a daily basis. Six selection strategies for improving lactation milk without decreasing persistency were compared: 1) index I(R1), subject to the restriction of equal genetic gains at days in milk (DIM) 60 and 280, 2) I(R2), subject to the restriction of zero gain at DIM 60, 3) desired gains index I(d), designed to increase lactation milk without altering the lactation curve, 4) index I(u), comprising lactation EBV and persistency without standardization, 5) index I(w), consisting of lactation EBV (EBV(L)) and persistency with standardization, and 6) conventional selection on EBV(L) and used as a basis for comparison. Of the 6 selection strategies compared, I(R2) yielded the greatest persistency, but achieved the smallest response in EBV(L), suggesting that it is impractical to increase persistency by inhibiting change in the peak yield. Index I(u) showed the same response in lactation milk as conventional selection on EBV(L), but resulted in the same decreased persistency. Although both I(R1) and I(d) achieved constant persistency, the former produced a greater lactation response (669 kg EBV) than the latter (560 kg EBV). Thus, I(R1) is a viable strategy for improving EBV(L) while holding persistency constant. None of the 6 selection strategies excelled in both lactation milk and persistency. Index I(w) appears to be a reasonable choice for improving both traits, although responses would depend on the relative economic importance of the 2 traits. Differential responses between I(u) and I(w) emphasize the need to weight the EBV of different traits by the inverse of their standard deviations in index construction when the EBV vary widely in variance. The general formula developed here provides a useful genetic means of modifying the lactation curve by restricting differential genetic gains among different days of the lactation.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".