Factors Affecting Weight Gain in Nelore Calves from Birth to Weaning in the Bolivian Tropic
Bibliographic record
Abstract
Retrospective data corresponding to the period between 2002 and 2018 were used, belonging to the Cooperativa Agropecuaria Integral San Juan de Yapacaní, Santa Cruz, Bolivia. Data corresponding to 663 male and female calves born to primiparous and multiparous cows were used. The calves had 13 individual weight controls. The calves had 13 individual weight controls. Variables used: Date of birth, Calf live weight at birth in kg, Calf live weight at weaning in kg, Average daily gain in kg, Live weight of cow in kg, Number of calving of cows. The mean values and standard deviations of the weights at birth were 35.1 ± 4.6 kg for males and 32.3 ± 4.7 kg for females, at 240 days (weaning) the weight of the males was 229 ± 35.8 kg and for females 206 ± 31.5 kg. The mean values and standard deviations of the weight increases were 0.807 ± 0.14 kg for males and 1.0 ± 0.13 kg for females. The selected model with the regressor variables: Year, Sex of the calf, Number of calving and Live Weight at Birth, all significant (p ≤0.001). No interaction was significant to be considered in the model (p≥0.05). The live weight at birth, the sex, the years and the number of deliveries of the mothers showed in this work and for the animals analyzed to be the factors affecting the increase in live weight in the rearing stage (from birth to weaning) in Nelore calves in the Bolivian tropics.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".