Bibliographic record
Abstract
Milk production and reproductive performance are major factors with respect to overall efficiency and profitability of the dairy industry. For many years, dairy cattle research breeding programs were mainly oriented towards yield traits in most countries. Exceptions were the Scandinavian countries, whose selection indices also included health and reproduction, and North American countries, whose selection indices included conformation together with production. Functional traits, such as fertility, longevity, and health traits, are of increased interest to producers in order to improve herd profitability. Various reports indicated that breeding for increased production in dairy cattle has negative side effects on health and fertility traits (Pryce et al., 2004; Washburn et al., 2002 and de Jong, 2006, Melendez., P and P. Pinedo. 2007; Sewalem et al., 2008). In a review of national selection indices, Miglior et al. (2005) indicated that the importance of reproduction traits in dairy cattle breeding programs has dramatically increased in the last five years. Several countries have included fertility traits in their national breeding objectives. However, direct selection for fertility traits may be inefficient because these traits have low heritability values (Jamrozik et al., 2005, and others) resulting in low accuracy of estimated breeding values, especially for cows and young bulls under testing when they get their first proofs. Therefore, decisions that would be made on early selection for these traits are associated with uncertainty. Moreover, selection for milk production has been carried out intensively for a long time and hence genetic evaluation of fertility traits might be biased by not accounting for selection decisions made on correlated traits. Walter and Mao (1985) reported that selection bias in genetic parameter estimates of traits undergoing sequential selection can be reduced if these traits are analyzed simultaneously with traits that did not undergo selection. Therefore, accounting for milk production in the genetic evaluation for fertility traits may avoid the bias on estimated genetic parameters and may also increase the accuracy of selection. The aim of this study was to assess the influence of including either milk production or heifer non-return rate as a correlated trait on the genetic evaluation of fertility traits in Canadian.
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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 | 0.005 | 0.008 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.014 | 0.005 |
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".