Assessment of the biochemical methane potential of mono- and co-digested dairy farm wastes
Bibliographic record
Abstract
This study aimed to evaluate the methane potential of mono- and co-digested dairy farm wastes. The tested substrates included manure from lactating, dry, and young cows, as well as waste milk and feed waste. The highest methane yield was achieved from the lactating cow manure, which produced an average of 412 L of CH 4 kg −1 volatile solids, followed by young and dry cow manures (332 and 273 L of CH 4 kg −1 volatile solids, respectively). Feed and milk yielded an average of 325 and 212 L of CH 4 kg −1 volatile solids, respectively. Co-digesting the manures from lactating and young cows with feed improved methane production by 7%. However, co-digesting the dry cow manure with feed achieved only 85% of the calculated methane yield. Co-digesting manure and milk at a ratio of 70:30 enhanced the methane potential from lactating, dry, and young cow manures by 19, 30, and 37%, respectively. Moreover, co-digesting lactating, dry, and young cow manures with milk at a ratio of 30:70 enhanced the methane yield by 60, 30, and 88%, respectively. The cumulative methane production of all samples was accurately described using the Gompertz model with a maximum error of 10%. Carbohydrates contributed the most to methane potential, while proteins and lipids were limiting.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".