Production of Jiggs bermudagrass and the impact of quality milk production and quality of Holstein dairy cows under an intermittent grazing system
Bibliographic record
Abstract
Dairy production plays a fundamental role in the Brazilian economy and high-quality forage is necessary for ruminants to produce satisfactory milk levels. The aim of the present study was to evaluate the production and quality of Jiggs bermudagrass and its effects on the production and quality parameters of milk from Holstein cows under an intermittent grazing system throughout the year. The experiment was conducted in a randomized design with the four seasons as treatments, replicated five times. The season had a significant effect on the production and nutritional parameters of Jiggs bermudagrass with the highest total dry matter production observed during summer, followed by spring and fall. The neutral detergent fiber and acid detergent fiber contents were significantly higher in winter. The in vitro dry matter digestibility was significantly higher in summer, spring, and fall. Jiggs bermudagrass is a promising forage for the enhancement of milk production under intermittent stocking. However, its effects vary seasonally which exerts a greater influence during the winter, even with irrigation, because it directly affects the milk production and quality. The correlation results demonstrated the importance of better quality forage for increasing milk production without compromising the levels of milk solids.
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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".