Estimation of Landfill Methane Emissions Using Stochastic Search
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Municipal solid waste (MSW) landfills can generate significant amounts of methane. There is considerable interest in quantifying surface methane emissions at such facilities. Numerous techniques exist for the evaluation of methane emissions from landfills. These techniques are either based on analytical emission models or on measurement methods. This paper presents a method to estimate methane emissions using ambient air methane measurements obtained on the surface of a landfill. Genetic algorithms (GA) based optimization combined with the standard Gaussian dispersion model are employed to identify locations as well as emission rates of potential emission sources throughout a MSW landfill. A case study is employed to evaluate the performance of the proposed methodology. GA-based search techniques are proven to be useful in estimating source locations and emission rates using methane concentration measurements.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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 it