Quantification of methane emissions from anthropogenic sources: A case study in Canada
Bibliographic record
Abstract
Anthropogenic methane emissions are generated in several economic sectors, including agriculture, waste management, oil and gas production, and others. Canada is one of the world’s largest oil and gas producers, ranks in the top-25 for agricultural production, and is the world’s largest waste producer per capita. As a result, the methane emission potential is high in parts of Canada where all these activities co-occur. To quantify emissions from multiple co-located sectors, we conducted a case study in Grande Prairie, a small city in Canada’s west dominated by oil and gas production and agriculture. Our goal in this study was to produce a gridded dataset of emissions for the Grande Prairie region. In November 2021, we measured atmospheric mixing ratios of methane using a high-precision gas analyzer mounted in a truck, and estimated emission rates using an inverse Gaussian plume model. During our campaigns, we passed downwind of roughly 220 oil and gas sites and 20 farms with grazing cattle or bison present. We detected emissions at about one-quarter of the oil and gas sites and one-third of the farms, and we also observed emissions from waste management and power generation facilities. Methane emissions from oil and gas production sites were relatively low compared to others we have measured in Canada, but despite this we still found that oil and gas was the dominant methane-emitting sector in the Grande Prairie region. The results of this study feed into a long-term methane monitoring study, focused on multiple economic sectors, methane source types, and detection approaches.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".