Predicción, del peso vivo en ganado bovino, a partir de mediciones corporales.
Bibliographic record
Abstract
This study took place in a cattle ranch working with double-purpose cattle, in the Department of Jutiapa, Guatemala, with the objective of calibrating a model of cattle measuring tape, with body measures, during the months of April, May and July, 1993. The measures were taken from 456 cattle heads, and the measuring variables comprehended: the torax diameter (TD), the body lenghth (BL), the live weight in kilograms (LW), and the age in years (AG). Cattle food was mainly pastures of “African Star” and “Jaragua” varieties, and other natural species. The herd produces milk all year round with a daily milking, and calves suckle until eight months old. The cattle measurements information was analyzed throughout fixed-effect models, including the TD, BL and AG variables, to determine the contribution of each effect for the live weight predictions. Two lineal multiple regressive models were adjusted by natural logarithm and by base-10 for males and females respectively. The analysis determined that in the studied population, the TD, BL and AG variables can be used to predict the live weight, according to the animal’s sex.
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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 | 0.000 | 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".