Pregnancy per artificial insemination and pregnancy loss in lactating dairy cows of a single herd following timed artificial insemination or insemination at detected estrus
Bibliographic record
Abstract
Thangavelu, G., Gobikrushanth, M., Colazo, M. G. and Ambrose, D. J. 2015. Pregnancy per artificial insemination and pregnancy loss in lactating dairy cows of a single herd following timed artificial insemination or insemination at detected estrus. Can. J. Anim. Sci. 95: 383–388. The objective of this retrospective study was to determine the factors affecting pregnancy per artificial insemination (P/AI) and pregnancy loss in lactating dairy cattle. Breeding records (n=1466) for 5 consecutive years were evaluated from one dairy herd. The effects of type of breeding [timed artificial insemination (TAI; n=1246) vs. insemination at detected estrus (IDE; n=220)], parity (primiparous vs. multiparous), body condition score (BCS; low ≤2.5 vs. high >2.5), year, season (summer vs. other seasons) and fertility group (high fertile <3 vs. low fertile ≥3 inseminations), on P/AI and pregnancy loss (i.e., late embryonic/early fetal loss, abortion and stillbirth) were determined using the GLIMMIX procedures of SAS software. Pregnancy per AI was influenced by type of breeding and season. Pregnancy per AI was lower (P<0.05) in cows that were TAI (28.7%) than IDE (37.1%) and during summer (30.3%) than other seasons of the year (35.5%). Pregnancy loss was higher (P<0.05) in low BCS (9.1%) than in high BCS (1.9%) cows. However, parity, year, and fertility group affected neither P/AI nor pregnancy loss.
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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 | 0.002 |
| 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) | 0.000 | 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 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".