Using Repeated Aerial Methane Measurements to Assess Inventory Protocols and Track Year-over-year Trends in Emissions
Bibliographic record
Abstract
Measurement-based inventories combining source-resolved aerial LiDAR measurements with bottom-up emission and activity data have provided unprecedented insight into the origins and magnitudes of oil and gas sector methane emissions and, in Canada, are now being used to inform methane estimates used in official national greenhouse gas inventory reporting. However, the protocols for creating measurement-based inventories are new and continue to be refined as both measurement technology and scientific understanding of the oil and gas sector improve. In this study, we examine independent, measurement-based inventory estimates derived from aerial survey data collected during 2020, 2021, 2023, and 2024 in the Canadian province of Saskatchewan; 2021, 2023, and 2024 in the province of British Columbia; and 2021 and 2023 in the province of Alberta. Data for each province are used to quantify region-specific sample size requirements, providing important insights into how prescribed sample sizes may need to vary depending on the characteristics of the production basin. Year-over-year emission trends are also examined in detail, which reveal varying degrees of success in reducing emissions in regions with distinct regulatory frameworks while highlighting key remaining sources to target for mitigation.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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".