Short Communication: Suppression of Estrous Cycles in Lactating Cows Has No Effect on Milk Production
Bibliographic record
Abstract
The decline in milk yield observed after peak production in dairy animals results from apoptotic death of mammary epithelial cells. In cows, this decrease in milk yield can be accelerated by injection of 17beta-estradiol, thus evoking a possible role of estrogens in the regulation of bovine mammary gland involution. In nonpregnant cows, mammary involution could be induced or enhanced by the return of estrous cycles and the accompanying cyclic peaks of estrogen concentration in the serum of lactating animals. To test this hypothesis, we inserted implants of a GnRH agonist, deslorelin, in an ear of each cow (n = 10) on d 10 and 100 of lactation, to temporarily suppress the return of ovarian cycles. Cows were studied from calving to d 210 of lactation. Deslorelin had no impact on feed intake or animal health. Deslorelin significantly reduced serum concentrations of 17beta-estradiol and progesterone as compared with untreated cows (n = 10). Deslorelin had no effect on milk fat and protein, whereas milk lactose content was lower in treated cows than in control cows on d 100 of lactation. Finally, there was no difference in milk production between the 2 groups of cows. These results are consistent with previous observations that showed that delaying estrous cycles after calving had no effect on milk yield and they extend those observations to late lactation. Based on milk production data, the estrogen profiles associated with recurring estrous cycles apparently do not cause bovine mammary tissue to undergo gradual involution.
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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) | 0.005 | 0.002 |
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".