Phenotypic and genetic parameters of reproductive traits in Tunisian Holstein cows
Bibliographic record
Abstract
Various factors influencing reproduction in dairy Holstein cows were routinely evaluated and genetic parameters were estimated for four traits for assessing fertility of artificially inseminated cows: Calving to first service interval (CFSI), calving interval (CI), calving to conception interval (CCI), and number of services per conception (NSC). Data used in this investigation consisted of records of insemination and calving events on Tunisian Holstein cows. Records were registered from 1994 to 2003 in 150 herds to study the effects of non-genetic factors and estimate the heritabilities of those fertility traits. The factors examined were: month and year of calving, herd, parity, and year-month of calving. The effect of month and year of calving (or insemination), herd, parity and year-month of calving were included in the model and were significant (P < 0.01) except for the number of lactations that does not have an effect on the number of services per conception. A decreasing efficiency in cow fertility was observed over the last years, with a longer day for first service interval. Heritability for fertility traits was low ranging from 0.027 for NSC to 0.067 for CI. The results suggested that more attention should be paid to herds with too low fertility traits and that monitoring/alert and intervention schemes should be tested in research/action approaches.
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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".