Melatonin implants in late pregnancy increase yield and enhance milk quality in dairy goats
Bibliographic record
Abstract
This study investigated the effects of melatonin implants in pregnancy on milk production and composition, and the quality of colostrum in dairy goats. Thirty days before kidding, 92 goats (group MEL) received one melatonin implant, and the remaining 177 goats (group CON) did not. Three monthly milk evaluations included milk yield (kg/day), composition (%Fat, %Protein, % Lactose), daily yield (Fat, g/day Protein, g/day Lactose). A sample of colostrum was obtained from 165 goats, from which its composition, and IgG concentration were measured. MEL had a significantly ( P < 0.01) higher milk yield in the second month (3.16 ± 0.10 kg/day) than CON (2.78 ± 0.07 kg/day). In the three milk samplings, fat concentrations were significantly higher ( P < 0.05) in the MEL than in the CON does. In the second milk sampling, does that had received a melatonin implant produced higher ( P < 0.05) daily milk yield components than did non-implanted (Fat: 144 ± 6.0 vs. 115 ± 3.4; Protein: 104 ± 3.4 vs. 91 ± 2.5; Lactose: 148 ± 5.8 vs. 131 ± 4.3 g/day). In conclusion, melatonin implants administered 30 days before kidding increased milk production, the amounts of milk daily components in the second month of lactation, and the concentration of fat milk.
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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 | 0.000 |
| 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) | 0.000 | 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".