74 Effects of particle size and levels of inclusion of selected engineered biocarbon on methane emission and rumen fermentation of barley-silage based diet in batch culture
Bibliographic record
Abstract
Abstract This in vitro study assessed the effects of three pine-based engineered biocarbons (CP002, CP016, CP023) that differed in particle size (< 0.5, 0.5–2.0, < 2.0 mm) at two inclusion levels (0.25 mg/ml and 0.5 mg/ml) on total gas and CH4 production as well as rumen fermentation parameters when added to a barley silage-based total mixed ration diet. Biocarbon products differed in bulk density, surface area, pore volume, and pH but had similar chemical compositions. The control consisted of the barley-silage diet only. In each jar, 0.5g DM of the total mixed ration was supplemented with the above biocarbon treatments. These jars were incubated for 48 hr at 39 °C. Gas samples were collected at 3, 6, 9, 12, 24, 36 and 48 hr. Data were analysed by using PROC MIXED in SAS in a randomized complete block design with treatment, rate, and particle size as fixed effects and run and replicate as random effects. Methane production was not affected by treatment (P = 0.37), inclusion rate (P = 0.57) or particle size (P = 0.39). Gas production linearly increased (P < 0.01) with biocarbon. Digested DM was not affected (P = 0.23) by biocarbon addition. Additionally, treatment, rate and particle size had no effect (P > 0.05) on pH, VFA and ammonia N concentrations. In conclusion, the amount of biocarbon differing particle sizes at different rates did not affect CH4 emissions but did increase gas production.
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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 | 0.001 | 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".