Genetic relationships between measures of temperament in Australian and French Limousin cattle
Bibliographic record
Abstract
Docile cattle are preferred by beef cattle breeders for ease of handling and management. Genetic correlations between temperament and meat quality traits in tropically adapted breeds indicate that more docile animals have more tender meat (Kadel et al., 2006). An EBV for docility was first introduced for Australian Limousin in 2000 and genetic progress has been made. Many Australian Limousin calves are sired by imported bulls, mainly from France, USA, Canada and UK. While animals in individual and progeny test stations in France have temperament records, currently an EBV for docility is not available. French AI sires account for a significant number of calves registered each year in Australia (8% in 2004), thus future genetic improvement in Australian Limousin for docility could be boosted by the documentation of genetic merit for docility in France. The aim of this study was to investigate genetic relationships between measures of docility in Australia and France.
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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 | 0.001 |
| 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".