PSXI-15 Effects of post-pyrolysis treated biochars on nutrient disappearance, methane production and ruminal fermentation of a silage-based diet in an artificial rumen system (RUSITEC)
Bibliographic record
Abstract
Abstract This study investigated the effects of post-pyrolysis treated biochar on nutrient disappearance, total gas and methane (CH4) production, rumen fermentation and microbial protein synthesis in an artificial rumen system (RUSITEC) fed a barley silage-based diet. The basal diet consisted of 60% barley silage, 27% barley grain, 10% canola meal and 3% mineral/vitamin supplement (DM basis). Three spruced-based biochars, treated post-pyrolysis with either zinc chloride, hydrochloric acid/nitric acid mixture or sulfuric acid were added at 2.0% of substrate DM. In a randomized complete block design, treatments were assigned to sixteen vessels (n = 4/treatment) in two RUSITEC systems. The experiment was conducted over 15 d, with 8 d of adaptation and 7 d of sampling. Nutrient disappearance of dry matter (DM), organic matter (OM), acid detergent fiber (ADF) and neutral detergent fiber (NDF) was determined after 48 h of incubation from d 9 to 12, and microbial protein synthesis was measured from d 13–15. Data were analyzed using PROC MIXED in SAS, with the fixed effect of treatment and random effect of RUSITEC system and vessel. Biochar inclusion did not affect disappearance of DM (P = 0.49), OM (P = 0.60), CP (P = 0.14), NDF (P = 0.48), ADF (P = 0.11) or starch (P = 0.58). Biochar also had no effect on total gas production (P = 0.31) or CH4 produced expressed as a % of total gas production (P = 0.06), mg/d (P = 0.70), mg/g of DM incubated (P = 0.74), or mg/g of DM digested (P = 0.64). No effect on total VFA (P = 0.56) or NH3-N (P = 0.20) production were observed. Neither microbial protein synthesis nor total protozoa count were affected by biochar addition (P > 0.05). In conclusion, biochar inclusion in a silage-based diet did not exhibit the potential to mitigate CH4 emissions or improve digestion in a RUSITEC system.
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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".