Weight at First Calving and Its Relationship With Productive Indicators in Nelore Cows in a Grazing System of the Bolivian Tropics
Bibliographic record
Abstract
Considering only selection for increased weight gain until after one year could cause adult cow weight gain that would not be desirable depending on the production system. To evaluate the relationship of weight at first calving and its relationship with productive indicators in Nelore cows in a grazing system of the Bolivian tropics. Retrospective data from the years 1992 to 2019 were used, which were part of two cooperatives: Agropecuaria Integral San Juan de Yapacaní and the Centro Tecnológico Agropecuario located in Santa Cruz de la Sierra, Bolivia. The data corresponding to 1052 Nelore primiparous cows were used for the research work. The lightest cows had the same calf weight at birth as the rest of the heaviest cows, weaned a lighter calf, arrived the first calving rapidly, showed no differences with the Accumulated Production and the Calf Index with the heaviest cows, but had the highest stock efficiency. Identifying the group of lightest cows as the most efficient was considered a positive tool to recommend producers to take into account at the time of selection. The study shows that the age at first calving is related to indicators of productive efficiency in Nelore cows in a grazing system of the Bolivian tropics and that their use would have a greater impact in identifying the most efficient cow for each production system.
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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".