Estimating total economic merit for the Portuguese Holstein cattle population under new economic conditions
Bibliographic record
Abstract
The objective of this study was to develop a total economic merit index that identifies more profitable animals using Portugal as a case study to illustrate the recent economic changes in milk production. Economic values were estimated following future global prices and EU policy, and taking into consideration the priorities of the Portuguese dairy sector. Economic values were derived using an objective system analysis with a positive approach, that involved the comparison of several alternatives, using real technical and economic data from national dairy farms. The estimated relative economic values revealed a high importance of production traits, low for morphological traits and a value of zero for somatic cell score. According to several future market expectations, three scenarios for milk production were defined: a realistic, a pessimistic and an optimistic setting, each with projected future economic values. Responses to selection and efficiency of selection of the indices were compared to a fourth scenario that represents the current selection situation in Portugal, based on individual estimated breeding values for milk yield. Although profit resulting from sale of milk per average lactation in the optimistic scenario was higher than in the realistic scenario, the volatility of future economic conditions and uncertainty about the future milk pricing system should be considered. Due to this market instability, genetic improvement programs require new definitions of profit functions for the near future. Effective genetic progress direction must be verified so that total economic merit formulae can be adjusted and selection criteria redirected to the newly defined target goals.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".