Effect of pine-based biochars with differing physiochemical properties on methane production, ruminal fermentation, and rumen microbiota in an artificial rumen (RUSITEC) fed barley silage
Bibliographic record
Abstract
This study investigated the effects of three pine-based biochar products on nutrient disappearance, total gas and methane (CH4) production, rumen fermentation, microbial protein synthesis, and rumen microbiota in a rumen simulation technique (RUSITEC) fed a barley-silage-based total mixed ration (TMR). Treatments consisted of 10 g TMR supplemented with no biochar (control) and three different biochars (CP016, CP024, and CP028) included at 20 g·kg−1DM. Treatments were assigned to 16 fermenters (n = 4 per treatment) in two RUSITEC units in a randomized block design for a 17 d experimental period. Data were analyzed using MIXED procedure in SAS, with treatment and day of sampling as fixed effects and RUSITEC unit and fermenters as random effects. Biochar did not affect nutrient disappearance (P > 0.05), nor total gas or CH4, irrespective of unit of expression. The volatile fatty acid, NH3-N, total protozoa, and microbial protein synthesis were not affected by biochar inclusion (P > 0.05). Alpha and beta diversity and rumen microbiota families were not affected by biochar inclusion (P > 0.05). In conclusion, biochar did not reduce CH4emissions nor affect nutrient disappearance, rumen fermentation, microbial protein synthesis, or rumen microbiota 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".