Estacionalidad en la producción de leche en un rebaño bovino
Bibliographic record
Abstract
Milk production seasonal performance was assessed from January 1999 to December 2005 on a typical dairy farm from the Livestock Center “Triangulo Uno” in Jimaguayu municipality, Camaguey province, Cuba. From Agust 2000 up to the year 2002, dairy cows were distributed into two milking groups according to their high or low productive performance. Throughout the whole assessed period, dairy herd management was carried out by the technical standards and feeding at milking time consisted in a grass and concentrate food diet. From 1999 throgh March 2002, a new diet including 15 % of torula yeast, 83 % of 2,8 % ammonified molasses, 1 % of sodium sulphate, and 1 % of mineral salt was / administered/. The analysis on dairy herd productive performance comprised the following variables: milk total production, kg milk/ha and kg/ milk/milker, kg milk/dairy cow/day total carrying capacity (dairy cows/ha), and total kg milk/dairy cow. The Alfa Laval cow-milk direct measuring device was used to find out milk production. Seasonality effect on milk production was assessed by a lineal general model. Time series analysis was applied to determine season role and milk production forecast. The highest values for /total/ milk production and kg milk/dairy cow were registered in 2001 due to feeding levels and dairy herd management. Seasonality effect on milk production indexes was proved, with a better productive performance during the rainy season (MayOctober). These indexes showed significant differences among the assessed years.
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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 | 0.001 | 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".