Potential methane emission reductions for two manure treatment technologies
Bibliographic record
Abstract
The effect of two dairy manure treatments, solid–liquid separation (SLS) and anaerobic digestion (AD), on methane potential and the speed of production was evaluated. Assays were performed in the lab to measure methane (CH4) production over 202 d from dairy manure samples taken before and after each treatment. Compared to raw manure, CH4 emissions on a per-L basis were reduced 81% by SLS and 59% by AD, on average. The mean (SD) ultimate CH4 emission potential (B0) per kg of volatile solids (VS) was 247 (8) L CH4 kg−1 VS for raw manure, 221 (9) L CH4 kg−1 VS for separated liquid, and 160 (4) L CH4 kg−1 VS for anaerobic digestate. Thus, SLS reduced the B0 of the liquid fraction by 11% and AD reduced B0 by up to 35% compared to raw manure. Manure treatment affected the speed of CH4 production: SLS increased the CH4 production rate and thus separated liquid manure was the fastest to produce 90% of the ultimate CH4 production. Therefore, both the speed of degradation and B0 should be considered when assessing these techniques for farm-scale manure storages, because actual emission reductions will depend on storage conditions.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".