Comment on amt-2023-124
Bibliographic record
Abstract
Abstract. Landfills are a significant source of fugitive methane (CH4) emissions which should be precisely and regularly monitored to reduce and mitigate net greenhouse gas emissions. In this study, we present long-term in-situ near-surface mobile atmospheric CH4 mole fraction measurements (complemented by meteorological measurements from a fixed station) from 21 campaigns that cover approximately four years from September 2016 to December 2020. These campaigns were utilized to regularly quantify the total CH4 emissions from an active landfill in France. We use a simple atmospheric inversion approach based on a Gaussian plume dispersion model to derive CH4 emissions. Together with the measurements near the soil surface mainly dedicated to the identification of sources within the landfill, measurements of CH4 made on the landfill perimeter (near-field) helped us to provide some qualitative insights about the respective weight of the main areas of emissions. However, we hardly managed to extract a signal representative of the overall landfill emissions from these measurements, which limited our ability to derive robust estimates of the emissions when assimilating them in the atmospheric inversions. The analysis shows that the inversions based on the measurements from a remote road further away from the landfill (far-field) yielded more reliable estimates. According to these estimates, the total CH4 emissions have a large temporal variability and range from ~0.4 t CH4/d to ~7 t CH4/d, with an average value of ~2.1 t CH4/d. We find a weak negative correlation between these estimates of the CH4 emissions and atmospheric pressure for the active landfill. However, this weak emission-pressure relationship is based on a relatively small sample of reliable emission estimates with large sampling gaps. More frequent robust estimations are required to better understand this relationship for an active landfill.
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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 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.020 | 0.011 |
| Insufficient payload (model declined to judge) | 0.266 | 0.195 |
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".