Breeding Soundness Evaluation and Semen Analysis for Predicting Bull Fertility
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Bull fertility is influenced by numerous factors. Although 20-40% of bulls in an unselected population may have reduced fertility, few are completely sterile. Breeding soundness refers to a bull's ability to get cows pregnant. A standard breeding soundness evaluation identifies bulls with substantial deficits in fertility, but does not consistently identify sub-fertile bulls. In this regard, the use of frozen-thawed semen (from bulls in commercial AI centres) that meets minimum quality standards can result in pregnancy rates that differ by 20-25 percentage points. Although no single diagnostic test can accurately predict variations in fertility among bulls that are producing apparently normal semen, recent studies suggested that a combination of laboratory tests were predictive of fertility. This review is focused on recent developments in prediction of bull fertility, based on assessments at the molecular, cellular and whole-animal levels.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| 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) | 0.000 | 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 it