Effect of canola oil supplementation level on total tract digestion, ruminal fermentation, and methane emissions of cows grazing Urochloa sp. supplemented with a fixed amount of concentrate
Bibliographic record
Abstract
Abstract Four rumen-cannulated cows ( Bos taurus × Bos indicus , 657 ± 92 kg body weight, BW) in a rotational grazing ( Urochloa sp.) system were assigned to different canola oil (CO) inclusion levels, 0.0, 0.40, 0.80, and 1.2 g/kg according to shrunk body weight (SBW, BW adjusted for gastrointestinal filling) in a 4 × 4 Latin Square design to evaluate CO on the CH 4 emissions and dietary energy intake. CH 4 emissions were estimated using an infrared analyzer methodology ( Sniffer method). Grass intake and fecal production were estimated using Cr 2 O 3 as an external marker. CO supplementation increased (linear effect, P ≤ 0.05) total dry matter and gross energy intake with a linear increase ( P = 0.09) in neutral detergent fiber (NDF) intake. While digestible energy (Mcal/kg) linearly increased with increasing CO supplementation level (linear effect, P < 0.05), total tract digestion of organic matter, NDF, and CP was comparable ( P > 0.05) between levels. Maximal CO supplementation (1.2 g/kg SBW) significantly decreased total ruminal protozoa population, acetate:propionate ratio, and enteric methane production (g/kg DMI) by 9, 5.3, and 17.5%, respectively. This study showed that, for cows grazing tropical forages, CO can be supplemented up to 1.2 g/kg SBW (5.8% of the total diet) without negatively affecting intake and nutrient digestion while reducing ruminal fermentation efficiency and enteric methane emission (≤ 17.5%).
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".