Bibliographic record
Abstract
The objective of this thesis was to evaluate genetic aspects of longevity (LPL) in the Chianina beef cattle population in order to define how to include this trait in selection criteria. The Chianina breed has been raised for over twenty-two centuries inItalyand today this breed is present in different countries across Europe, South and Central America,Australia,Canada and the USA. Its characteristics of somatic gigantism and rapid growth are combined with enormous resistance to harsh environmental conditions, great ease of calving and an excellent meat quality. In this breed longevity was recorded as the length of productive life (LPL), defined as years from the age at the insemination that resulted in the birth of the first calf to the date of culling or censoring. Six mo were added after the last date of calving to account for the time that the calf remains with the cow. The LPL was equal to 5.97 years on average. Heritability was equal to 0.11 when both censored and uncensored data were included to estimate longevity with the survival analysis. Type traits were used as an early predictor of profitability and muscularity traits were the most important parameters for longevity among the factors studied. Cows with approximately one calf per year remained in the herd longer than cows with fewer calves.Cows with a long LPL were more profitable than cows with short LPL. The final score could be used as an early predictor of profitability. An increase of one day unit in LPL was associated with an increase of +0.19 /cow per year and +1.65 /cow on a lifetime basis. Including longevity in both the Chianina breeding index and breeding goal either using empirical or economical weights has the positive effect of increasing the response (+2.97 and +4.92 days/year respectively). Beef breeding organizations should consider the opportunity to include longevity in a future breeding scheme to increase profit and to promote the well-being and welfare of the cows.
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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 | 0.000 |
| 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".