Economic values of traits for dairy cattle improvement estimated using field-recorded data
Bibliographic record
Abstract
The objective of this study was to compute economic values of traits using an empirical approach. The data set consisted of 193 257 lifetime records of Holstein and Ayrshire cows. Different profitability measurements were used as the dependent variables in covariance models to compute different sets of economic values. A kilogram genetic increase in fat production had economic values between $25 and $31 in Holstein herds and between $34 and $36 in Ayrshire herds using lifetime profit as the dependent variable. A unit genetic increase in conformation had the highest positive impact on profit ($176 in Holstein herds and $300 in Ayrshire herds) while a similar increase in capacity had a negative impact on profit (between $–30 and $–102 in Holstein herds and $–92 in Ayrshire herds). Using lifetime profit adjusted for the opportunity cost of postponed replacement reduced the influence of type traits on profit. Finally, profits of first lactations were used to study the consequences of changes in pricing systems that occurred in Quebec in August 1992. A kilogram genetic increase in protein production had negative economic values in the 1980s ($–3.70$ in Holstein herds and $–8.33 in Ayrshire herds) and positive economic values after August 1992 ($7.50 in Holstein herds and $12.83 in Ayrshire herds). Key words: Dairy cattle, economic value, field-recorded data, profitability, estimated breeding value
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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 | 0.012 |
| 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 | 0.001 | 0.001 |
| 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".