Comparison of Landfill Methane Emission Quantification Using Multiple Observation Methods
Bibliographic record
Abstract
High Resolution Image Download MS PowerPoint Slide Quantifying facility-level methane (CH 4 ) emissions is an important task for measuring progress toward net zero and carbon emission reduction targets. Landfills are a significant source of anthropogenic CH 4 emissions in Canada. Quantifying Canadian landfill emissions is also critical for validating assumptions in bottom-up inventory calculations but is a challenging task because of their variability in emissions sources and complex topography on and near landfill sites. We compare CH 4 emissions estimates for seven different emissions quantification strategies and platforms at a large landfill in Southern Ontario, Canada. We compare ground-based, aircraft-based, and satellite-based remote sensing techniques in addition to ground-based stationary, mobile, and aircraft-based in situ observation strategies across a 3.5-year period, including a 28-month deployment of a low-precision sensor network for continuous monitoring. Each methodology quantified a large range of emissions rates that vary by 1 order of magnitude for the site (∼200–2000 kg·h –1 ), and the average estimated emissions rates agree within uncertainty. We find that the remote sensing methods have a higher empirical minimum detection limit and are sufficient for quantifying 20–50% of all Canadian landfill sites, while ground-based in situ methods have detection limits suitable for quantifying emissions from the majority of accessible landfill sites.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".