Influence of lameness on follicular growth, ovulation, reproductive hormone concentrations and estrus behavior in dairy cows
Bibliographic record
Abstract
The objective of this study was to examine the effect of a chronic stressor, lameness, on reproductive parameters. Seventy cows 30-80 days post-partum were scored for lameness and follicular phases synchronized with GnRH followed seven days later by prostaglandin (PG). Fifteen Lame animals did not respond to GnRH ovarian stimulation. Milk progesterone for 5 days prior to PG was lower in the remaining Lame cows than Healthy herdmates. Fewer Lame cows ovulated (26/37 versus 17/18; P = 0.04) and the interval from PG to ovulation was shorter in Lame cows. In Subset 1 (20 animals), the LH pulse frequency was similar in ovulating animals (Lame and Healthy) but lower in Lame non-ovulators. An LH surge always preceded ovulation but lameness did not affect the interval from PG to LH surge onset or LH surge concentrations. Before the LH surge, estradiol was lower in non-ovulating cows compared to those that ovulated and estradiol concentrations were positively correlated with LH pulse frequency. In Subset 2 (45 cows), Lame ovulating cows had a less intense estrus than Healthy cows, although Lame cows began estrus and stood-to-be-mounted earlier than Healthy cows. In conclusion, we have identified several parameters to explain poor fertility in some chronically stressed animals. From 30 to 80 days post-partum, there was a graded effect that ranged from 29% Lame cows with absence of ovarian activity, whereas another 21% Lame cows failed to express estrus or ovulate a low estrogenic follicle; in 50% cows, many reproductive parameters were unaffected by 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".