First Quantitative Assessment of Anthropogenic Methane Sources Investigated by the CHARM-F Lidar during the CoMet 2.0 Arctic Airborne Campaign
Bibliographic record
Abstract
The CoMet 2.0 Arctic airborne greenhouse gas measurement campaign took place over Canada in Summer of 2022. For the campaign, the German research aircraft HALO has been equipped with various instruments for remote-sensing and in-situ measurements of CO2 and CH4 and flown over target areas with potential sources of greenhouse gases, either natural (wetlands, thawing permafrost, etc.) or anthropogenic (oil and gas drilling sites, oil-sand mining, open-pit coal mines, landfills as well as biomass-burning in forest fires). With the city of Edmonton, Alberta as campaign base, a variety of sources of methane released due to human activity and adding substantially to the Canadian anthropogenic CH4 budget were conveniently within reach for our measurements.This presentation focuses on a selection of anthropogenic sources of CH4 in Canada as well as the Valdemingómez and Pinto landfill sites near Madrid, which were targeted during a test flight. We show first results of the evaluation of active remote-sensing measurements that were conducted with DLR's CHARM-F lidar system. By using the Integrated-Path Differential-Absorption (IPDA)-lidar technique, CHARM-F enables measurements of total column concentrations of methane and carbon dioxide along flight tracks. After further adding wind information from auxiliary measurements or models, emission fluxes from localized sources can be estimated. We will highlight the top emitters in terms of estimated emission rate of CH4 (in the 10kt/year range). Those are likely the most promising candidates for mitigation attempts.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".