PSI-1 Effects of source and level of inclusion of engineered biocarbon in a total mixed beef cattle diet on in vitro methane emissions and fermentation parameters
Bibliographic record
Abstract
Abstract This in vitro study evaluated seven different engineered biocarbon products supplied at three levels (0.5, 1.5 and 2.5 mg/ml inoculum) to determine their effects on total gas, methane production, and fermentation parameters when added to a barley silage-based diet. The biocarbon sources were derived from either coconut (CP001 and CP014) or pine (CP002, CP015, CP016, CP023, CP024) and differed in their physical properties and chemical composition. The coconut biocarbon sources were lower in pore space, particle size distribution and surface area but higher in bulk density than the pine products. The control consisted of only the barley-silage diet. The in vitro batch culture jars were incubated for 24 h at 39°C at the above inclusion levels in 0.5 g of diet. Gas samples were collected at 3, 6, 9, 12, 18 and 24 h and DM disappearance, pH, VFA and ammonia concentrations post incubation were measured. Data were analysed using the PROC MIXED in SAS as a randomized complete block design with treatment and rate as fixed effects and run and replicate as random effects. Total gas production was not affected by source of biocarbon (P = 0.85) and inclusion rate (P = 0.91). Cumulative methane (ml/g DM) had no response to biocarbon addition (P = 0.40) at any inclusion level (P = 0.48). Additionally, concentration of total VFA was not affected by treatment (P = 0.31) or inclusion rates (P = 0.25). NH3-N concentrations responded quadratically (P < 0.001) to all types of biocarbon. Higher inclusion rates of biocarbon linearly (P < 0.002) decreased feed digestibility, particularly the coconut-based biocarbon sources CP001 and CP014. In conclusion, supplementation of biocarbon to a TMR diet did not reduce methane emissions, but at higher levels of inclusion diet digestibility was negatively affected.
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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) | 0.001 | 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".