Methaanoxidatie bij mestopslagen : Voortgangsverslag deel 1: werking en aandachtspunten voor 3 methaanoxidatie technieken
Bibliographic record
Abstract
This project has focussed on researching the possibilities of capturing methane at manure storages and subsequently convert (oxidise) it into CO2. For three types of manure storages (manure bags, manure silos and manure basins) a test was performed to capture methane with as few as possible adjustments to existing storage facilities. Subsequently, a pilot was carried out with three different methane oxidation techniques with manure bags; chemical oxidation with a torch and biologically oxidation with a biofilter and a soilfilter. The first tests show that for all three techniques it is possible to oxidize methane with an efficiency of >95%, 60-80% and 60-70% respectively. Calculations with a methane production model and a cost analysis show that cost efficiency is highest if manure is stored as quickly as possible in a manure storage coupled to a methane oxidation technique. The field filter is the cheapest solution, but the total amount of methane converted is higher with flaring and the biofilter. In addition, the parties who will be involved in, among other things, licensing and control of the techniques were interviewed. The interviews show that assurance of the continued working of the technique and enforcement will play a crucial role in the acceptance of the techniques in livestock farming.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.016 | 0.003 |
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".