Animal Performance with and without Supplements in Mombaça Guinea Grass Pastures during Dry Season
Bibliographic record
Abstract
The objectives were to: 1) evaluate the potential of the animal performance on Panicum maximum cv. Mombaça (mombaça guinea grass) pasture during dry season, and 2) determine the effects of protein or protein-energy supplementation on liveweight gain of steers grazing guinea grass pasture. A complete randomized block design was employed with three treatments and three replicates. Treatments included non-supplemented animals (NS) and animals supplemented with protein (PS) or protein-energy (PES), provided at 0.15% or 0.6% of body weight for 115 days, during dry period. We used 36 weaned calves from initial bodyweigh (LW) of 192 kg (±5 kg) on nine plots of mombaça guinea grass (1.25 ha each). Each month, animals were weighed and pastures sampled to estimate forage characteristics. The average daily gain (ADG) was greater (P = 0.0001) for cattle fed supplement than for cattle fed no supplement (250 g steer-1), and greater for protein-energy supplement (770 g steer-1) than for protein supplement (460 g steer-1). Mombaça guinea grass pastures with 45 cm height at the end of the wet season have enough forage mass for maintenance throughout the dry season about 1.4 AU ha-1 (AU = 450 kg BW), and reasonable nutritive value (average of 8,1% of crude protein and 55,3% of in vitro organic matter digestibility) to provide small gains. Considering the nutritive value of Mombaça guinea grass during the dry period, protein and energy supplementation is required for weaned calves to optimize their performance.
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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".