Calidad de la dieta y leche de cabras en pastoreo extensivo en un ecosistema semiárido
Bibliographic record
Abstract
The quality of goat milk in extensive grazing systems is closely related to the biochemical composition of the plant species consumed, which varies seasonally. The objective of this study was to analyze the milk quality of Creole goats and the proximate composition of their diet during two seasons (dry and rainy) in a semi-arid ecosystem in Baja California Sur, Mexico. Data was analyzed using a completely randomized design with three replicates. Milk samples were analyzed using MilkoScan® Minor and Fossomatic® Minor equipment, while plant species composition was assessed using standard laboratory methods. Milk results between seasons showed significant differences (p ≤ 0.05) in fat (4.72%), protein (3.84%), lactose (4.55%), total solids (15.66%), and non-fat solids (10.45%) content, being higher during the rainy season. The proximate composition of the species consumed by goats also showed significant differences in moisture, ash, lipids, crude fiber, and energy, both between seasons and between species. These dietary changes were shown in the nutritional quality of the milk, which has relevant implications for the production of artisanal dairy products. It is concluded that seasonal variability in diet quality directly influences the physicochemical parameters of goat milk, which are greater during the rainy season. This can be exploited by local producers to improve the added value of their products.
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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 | 0.000 | 0.000 |
| 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".