Effect of day length and exogenous melatonin on chemical composition of sheep milk
Bibliographic record
Abstract
Abstract. Changes in day length are a major factor in the productivity of farm animals showing reproductive seasonality. Parameters of milk production in sheep and goats are dependent on melatonin and prolactin concentrations. Changes in the prolactin secretion in lactating sheep have an effect on the amount of milk produced, synthesis of milk proteins, fat and immunoglobulins (i.e. milk composition). In the end the quality and commercial value of milk is determined as well. The aim of this study was therefore to find out the effect of day length and exogenous melatonin on the chemical composition of milk and the level of fatty acids. Subjects were 60 Polish Longwool Sheep. Animals were randomly assigned to three groups, Group I (n=20) – ewes raised under natural day length; Group II (n=20) ewes raised under natural day length and were implanted with melatonin; Group III (n=20) – ewes exposed to an artificially short photoperiod (16D:8L). Sheep were milked twice daily using the Alfa Laval Agri milking machine. Composite milk samples were collected every 28 days to determine chemical composition and fatty acid content. The results obtained showed that the administration of exogenous melatonin and the simulation of a short-day photoperiod during the summer period had significant effects on the milk levels of solids, protein, fat and lactose, and on the fatty acid content of sheep 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) | 0.001 | 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".