Review: The use of bull breeding soundness evaluation to identify subfertile and infertile bulls
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
Efficient and economical herd management depends a great deal on maintaining a short, well-defined calving season. This requires highly fertile females and bulls. Low pregnancy rates are very noticeable, however; potentially greater economic loss may be due to delayed conception. Many studies showed that approximately one of every five bulls had inadequate semen quality, physical soundness, or both, but when evaluation of serving capacity is included about one in four bulls is unsatisfactory. Due mainly to the time and expense that the market will bear, usually only physical soundness and semen quality are evaluated. Breeding soundness evaluation is a useful, low-cost screening method for reducing the risk of using low fertility bulls. The biggest problem with breeding soundness evaluations is not our lack of knowledge or ability, but in the willingness of veterinary schools to provide adequate equipment and training in this area, a lack of diagnostic laboratories equipped to handle the more difficult cases and, most importantly, the weaknesses of human nature that result in negligent testing procedure.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.000 | 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