Modeling Lactation Curve in Primiparous Beef Cattle
Bibliographic record
Abstract
The work describes lactation curves and compares two methods to estimate milk yield (MY) in a grazing beef cattle herd of the EEBR Station-Udelar, Uruguay. Twenty-four Hereford, Angus and F1-crossbreed primiparous cows were used to estimate MY once a month, from birth to weaning, by weigh-suckle-weigh (WSW) technique and milking-machine (MM). Milk yield (MY), milk yield retained energy (ReMY), and calf weight were analyzed as repeated measures in a model including: sex of calves, month of lactation, cow and calf breed, milking method, estimation day (1 or 2), and post-partum days as fixed effects, and cow nested within breed as the random effect. The correlation analysis and the Gage r&R coefficient (repeatability and reproducibility) between the two methods were used to study their associations. Lactation curves were compared (AICC and BIC) using Wood (1964), and Jenkins and Ferrel (1984) models. The MY estimated differed with the methodology being WSW higher than MM (P < 0.001). The r&R coefficient (0.83) suggest lower associations between WSW and MM, being 18% and 6% the coefficients of variation, respectively. Cow breed was not significant for MY. Calf live weight and ReMY were negatively associated (-0.52, P < 0.0001). Based on variability observed, MM is more accurate to estimate MY and Wood curve the most adjusted to describe lactation in grazing beef cattle.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".