Barley or black oat silages in feeding strategies for small-scale dairy systems in the highlands of Mexico
Bibliographic record
Abstract
High costs from external inputs in small-scale dairy systems (SSDS) and possible effects of climate change, require forage alternatives as silage for the dry season, from small-grain cereals that have short cropping cycles, winter hardiness, and good nutritional quality. The objective was to assess the provision of 10 kg dry matter (DM) cow−1 d−1 of barley (BLY) or black oat (BKO) silages in three treatments: T1 = 100% BLY; T2 = 50% BLY + 50% BKO; T3 = 100% BKO for milking cows. All treatments also received 4.6 kg DM cow−1 d−1 of concentrates and access to pasture. Nine Holstein cows in groups of three were randomly assigned to a 3 × 3 Latin square design repeated three times, with 14 d experimental periods. Measurements of animal variables and sampling for chemical analyses of feeds were done during the last 4 d of each period. Feeding costs were by partial budgets. There were no differences (P > 0.05) for milk yield, milk fat and protein content, milk urea nitrogen, body condition score, or live weight. The cost of BLY silage was 8% less than BKO silage. T1 had the higher margin over cost of feeds followed by T2. Both silages alone or in combination are viable options for SSDS, as there were no differences in performance, or in feeding costs or margins.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".