Active and inactive oil and gas sites contribute to methane emissions in western Saskatchewan, Canada
Bibliographic record
Abstract
The oil and gas industry is Canada’s largest contributor to national methane (CH4) emissions. To quantify the input of active and inactive (suspended and abandoned) oil and gas infrastructure to regional CH4 budgets, we conducted truck-based measurements (transect-based and OTM 33A) with a greenhouse gas analyzer, complimented with optical gas imaging at oil-producing sites of Saskatchewan, including understudied regions. We found that inactive sites regionally accounted for roughly 43% of total measured CH4 emissions in Lloydminster, 9% in Kindersley, and 0% in Swift Current. Thus, CH4 emissions from oil production in southwestern Saskatchewan are underestimated by almost 25% if emissions from inactive sites are ignored. Measured mean CH4 emissions of actively producing oil and gas infrastructure in Lloydminster were at least 50% lower (36 ± 7 m3/day) than found in previous studies potentially due to declines in production schemes, effective implementation of emission reduction approaches, or spatial differences between sampled sites. Unlike previous studies, measured emissions in Lloydminster were lower than reported values (147 ± 10 m3/day). In contrast, measured emissions in Kindersley (64 ± 17 m3/day) and Swift Current (23 ± 16 m3/day) were close to reported emissions despite observed tank vents and unlit flares. Unlit flares emitted at least 3 times more CH4 than other infrastructure types and were the “super-emitters” in this study. Currently, provincial and federal regulations target only active infrastructure, but regulators may consider extending regulations to inactive sites where data suggest significant emission reduction potential.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".