81 Effects of engineered biocarbons on total gas and methane production, rumen fermentation and microbial protein synthesis in a semi continuous fermentation system (RUSITEC)
Bibliographic record
Abstract
Abstract This study investigated the effects of engineered biocarbon on nutrient digestibility, rumen fermentation, total gas and methane (CH4) emissions, and microbial protein synthesis in a rumen simulation technique (RUSITEC) fed a barley silage-based TMR. The basal diet consisted of 60% barley silage, 27% barley grain, 10% canola meal and 3% minerals. Three pine-based biocarbon products CP016, CP024 and CP028. were added at 2% of substrate DM. Biocarbons differed in bulk density, surface area, pore volume, pH, but had similar chemical compositions. Treatments were assigned to sixteen vessels (n = 4/treatment) in two RUSITEC apparatuses in a randomized block design. The experiment period was 17 d, with a 10-d adaptation and 7-d sample collection period. Data were analyzed using the PROC MIXED in SAS, with treatment (T), day (D) and TxD interactions as fixed effects and RUSITEC apparatus and fermenters as random effects. Compared to the control, biocarbon did not affect total gas (P = 0.98), the amount of CH4 produced per unit of DM incubated (P = 0.48) or per unit of DM digested (P = 0.27). Biocarbon treatments averaged 6.5 g of CH4 /g DM incubated and 9.06 g CH4 /g DM digested as compared to 7.1 g of CH4 /g DM incubated and 10.46 g CH4 / g DM digested in the control, respectively. Biocarbon CP024 had the greatest numerical reduction, followed by CP028 then CP016 in all CH4 associated parameters. Biocarbon addition did not affect the disappearance of DM (P = 0.63), OM (P = 0.34), CP (P = 0.48), NDF (P = 0.12), or VFA (P = 0.65) and ammonia N levels (P = 0.99) and protozoal counts (P = 0.72). The amount of bacterial nitrogen (mg/d) associated with feed particles increased (P < 0.003), suggesting that biocarbon may have enhanced colonization. In conclusion, engineered biocarbon did not reduce CH4 emissions in the RUSITEC.
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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) | 0.000 | 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".