Modifying the Lactation Curve to Improve Lactation Milk and Persistency
Bibliographic record
Abstract
Daily, stage and lactation estimated breeding values (EBV) and the shape of the lactation curve for each cow are controlled by a unique set of random (genetic) regression coefficients under a test day model, thus providing a basis for genetic improvement of these characteristics. Three selection procedures were developed for simultaneous improvement of total lactation milk and persistency: 1) index selection based on daily EBV, 2) index selection based on stage EBV, and 3) index selection based on random regression (RR) coefficients. A numerical example was given to demonstrate the computation of indexes based on stage EBV and based on RR coefficients. A conversion equation was derived to convert between genetic changes in EBV and RR coefficients. Index selection based on daily EBV would require the finding of 305 weighting factors for a lactation period of 305 d, making it impractical to determine the weighting factors on a daily basis. Alternatively, a lactation period was partitioned into a few stages to facilitate the construction of index selection based on stage EBV and index selection based on RR coefficients. These selection procedures make use of the annual genetic gains routinely computed in national genetic evaluations to restrict the genetic gains between different lactation stages to achieve the desired curve. When there is no prior knowledge of annual genetic gains, the proportional restriction of genetic gains between stages may be used. In summary, this study provides a simple means of modifying the lactation curve by manipulating genetic changes in different lactation stages at a pre-specified rate.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".