PSVII-10 Evaluation of different biochar sources added at two inclusion levels in a grass hay- based diet on dry matter disappearance and ruminal fermentation parameters in vitro
Bibliographic record
Abstract
Abstract This study evaluated the impact of adding biochar to a grass hay-based diet on in vitro dry matter disappearance (DMD), total gas production (GP), methane production (CH4), volatile fatty acid (VFA) and ammonia nitrogen (NH3-N) concentration. Treatments were arranged in a factorial design with 7 biochar products differing in origin (pine or coconut-based) and physical properties (course, fine or loose spheres) at 2 inclusion levels (2.25% and 4.5% of DM) and a grass hay-only control. On each three successive weeks, rumen fluid was collected from 2 ruminally cannulated beef heifers fed a grass hay-based diet, pooled and mixed with a buffer solution (1:2) and treatments incubated in triplicate for 48 h. Gas pressure was recorded at 3, 6, 9, 12, 18, 24, 36 and 48 h of incubation, and gas was collected at each time to measure CH4. At 48 h, pH was measured and the supernatant sampled for determination of VFA and NH3-N, while the residue was used to estimate DMD. Dry matter disappearance decreased (P < 0.05) with increasing addition of biochar. Biochar did not affect GP or CH4, but did reduce (P < 0.05) gas production per g of DM incubated (111.7 vs 107.8 ml/g), but not per g DMD. The lower GP per g DM incubated could be related to the increased proportion of the diet as biochar, without it contributing to GP. Biochar, irrespective of level did not affect the total or molar proportions of VFA. There was a biochar x inclusion level interaction (P < 0.04) in NH3-N concentration. Biochar type and inclusion level in the present study had little or no significant effect on in vitro fermentation and did not mitigate CH4 production from a grass hay-based diet.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".