Evaluation of influence of milk urea nitrogen on reproductive performance in smallholder dairy farms
Bibliographic record
Abstract
OBJECTIVE: The aim of this study was to determine the relationship between milk urea nitrogen (MUN) and reproductive performance in dairy cows in western Thailand. DESIGN: All cows calving from November 2014 to April 2015 were included in the study, a total of 486 cows from 47 farms. Each cow had milk constituents and MUN tested monthly up to confirmed conception or until the 8th month after parturition. Each farm had a dietary assessment completed. Cox proportional hazard models with shared frailty were used to determine associations of conception rate. RESULTS: Cows became pregnant increasingly quickly over time, except during 100-150 days of lactation. A change in MUN from 12.5 to 13.5 mg/dL on the closet day to breeding was associated with a 5% decrease in conception. Milk protein was negatively associated with hazard of conception, whereas milk lactose and dietary protein:energy ratio had positive associations with conception rate. Breeding season was also significant; the highest conception rate was observed in cows inseminated during winter, whereas insemination during the humid rainy season resulted in the lowest conception rates. The farm random effect in the model was strongly significant. CONCLUSION: Detrimental effects of higher MUN on rate of conception were identified. The rate of conception was positively associated with protein:energy ratio in the study. Therefore, good nutritional management leading to positive energy balance should benefit conception rates.
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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 | 0.001 | 0.002 |
| 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".