Impact of genetic selection on management of boar replacement, Theriogenology
Bibliographic record
Abstract
Boars in an artificial insemination centre have been selected for their superior genetic potential, with ‘superior ’ being defined as having traits the customer wants transmitted to his herd. The ability to meet the customers ’ needs depends on the heritability of the trait, the geneticist’s success in devising a selection scheme for the trait in balance with other economically important traits, and the boar’s ability to produce sperm that can fertilise oocytes. Genetic evaluation research over the past 20 years has greatly increased the number of traits for which a boar can be selected: currently in the Canadian national program, these include age at 100 kg, backfat at 100 kg, feed efficiency, lean yield and litter size. In the near future, traits that are very likely to be added to this selection list include piglet survival, marbling, loin eye area and structure traits. In Canada, sires are ranked on two estimated breeding value (EBV) indices; one, focused on development of terminal sire lines, is based on the growth and yield traits and another, primarily focused on maternal line development, de-emphasises these traits and incorporates litter size. Boars that are in Canadian AI centres because of their excellent growth traits are typically in the top 5–10 % of the national population for terminal sire line index, but they may be only average or
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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 | 0.003 | 0.004 |
| 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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".