A laboratory-scale comparison of compost and sand—compost—perlite as methane-oxidizing biofilter media
Bibliographic record
Abstract
Municipal solid waste landfills produce methane, a potent greenhouse gas. A treatment approach is to passively vent landfill gas through a methane-oxidizing biofilter medium, a porous substrate that facilitates the growth of methanotrophic bacteria. Two substrates, compost and a sand-compost-perlite (SCP) mixture, were evaluated in a laboratory-scale experiment for their suitability as biofilter media. The SCP mixture was investigated to minimize settlement and was based on a particle size distribution specification used for turf grass. The long-term (218 days) methane removal rates showed that both compost and SCP were capable of removing 100% of the methane influent flux (134 g CH(4) m( -2) day(-1)). The post-experiment analysis showed that compost had compacted more than SCP. This did not affect the results; however, in a field installation, traffic on the biofilter surface (e.g. maintenance) could cause further compaction and negatively affect performance. Exopolymeric substance produced by the methanotrophic bacteria, attributed by others for declining removal rates due to bio-clogging, was not observed to affect the results. The maximum exopolymeric substance values measured were 23.9 and 7.8 mg D-glucose g(-1) (dry basis) for compost and SCP, respectively.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".