Influence of somatic cell count, body condition and lameness on follicular growth and ovulation in dairy cows
Bibliographic record
Abstract
The objective of this study was to investigate the effect of somatic cell count (SCC), body condition score (BCS) or lameness score on ovarian follicular growth and ovulation in dairy cows. Seventy four animals 30-80 days post-partum were monitored for all three conditions before synchronization of ovarian follicular phases by administration of gonadotrophin releasing hormone (GnRH) followed seven days later with prostaglandin F2alpha (PG). Ultrasonography of both ovaries twice daily throughout the follicular phase revealed that fewer animals with combined high SCC and lameness (4/9) ovulated compared to healthy animals (19/21; P=0.006) or animals with only high SCC (11/11; P=0.004) or only lameness (21/27; P=0.06). Overall, regardless of the presence of other concurrent conditions, fewer lame cows ovulated than Non Lame animals (30/42 and 30/32; P=0.015). Mean follicular growth and maximum follicular diameter were unaffected by any of the three conditions. However, dominant follicle growth and maximum diameter were greater in the 60 animals that ovulated compared to the 14 that did not; 1.83+/-0.16 versus 0.96+/-0.26mm/day (P=0.014) and 19.4+/-0.4 versus 16.4+/-1.2mm (P=0.003), respectively. In conclusion, lameness reduced the proportion of cows that ovulated and the synergistic effect of high SCC and lameness reduced that proportion further. However, follicular growth and maximum follicular diameter were unaffected by high SCC, low BCS or lameness.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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) | 0.001 | 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".