A controlled release experiment for investigating methane measurement performance at landfills
Bibliographic record
Abstract
We assessed the performance of various methane measurement solutions in landfill applications. A measurement solution is defined as a system or market offering that quantifies and/or localizes emissions. Our study involved full-scale multipoint- and area-source (dispersed) controlled releases of methane from the ground surface of a closed 25-hectare landfill with collection system and a background emission rate of 24 kg/hr. Most quantification methods performed well, but the truck-based Tracer Correlation method performed the best with an uncertainty of ±20%. Drone flux plane methods also performed well with an uncertainty of ±34% with very few outliers in the best-case scenario. For leak detection, aerial LiDAR demonstrated a 100% detection probability down to the lowest emission rates whereas drone column sensors emulating EPA 21 Surface Emissions Monitoring (SEM) were 100x less sensitive. Continuous sensors, trucks, and other methods were also assessed. Results indicate that many of the current quantification methods are effective, and potentially more accurate than first-order decay models, though they still need to be applied in a replicated fashion for robust site level estimates. Detection outcomes were variable, and questions remain, such as how the evaluated methods would compare the against regulatory SEM method, the impact of spacing and trigger thresholds (which differ regionally in regulation), and what detection level is actually necessary for effective landfill gas management. This site provides a future test bed for answering these other questions.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".