Methane Growth Rate Estimation and Its Causes in Western Canada Using Satellite Observations
Bibliographic record
Abstract
Abstract In this study, the GOSAT Proxy Retrieval (v9.0) data product of column‐averaged dry‐air mole fractions of atmospheric methane (XCH4) for the period 2009–2019 was analyzed to detect methane (CH4) trends in the three western Canadian provinces where oil and gas development activities have changed significantly over the last decade. Although we found statistically significant increasing XCH4 trends in all subdomains (northeast British Columbia‐NE, Alberta‐AB, southern Saskatchewan‐SK), XCH4 trends are not higher than the background trend (7.25 ± 0.30 ppb/yr) and enhancement trends (ΔXCH4, after removing the background quantity) are not detectable at any subdomain during 2009–2019. For further insight into trends in all subdomains, we divided the whole period (2009–2019) into two shorter periods (2009–2013 and 2014–2019) and estimated trends. We found XCH4 trends are higher than background trends particularly in the AB and SK subdomains during 2009–2013, and their ΔXCH4 trends are positive and also marginally statistically significant. However, we do not find any detectable ΔXCH4 trend if we consider either long‐term (2009–2019) or the second shorter period (2014–2019), suggesting local emission sources are dominating year to year fluctuation. From the source attribution analysis, we found both wetland and oil and gas sectors are controlling the CH4 growth rate in western Canada, but the oil and gas sector is the dominant driver in NE and SK subdomains. We also found the satellite‐based average ΔXCH4 trend (15.43 ± 8.19%/yr) between 2009 and 2013 likely reflects a trend in oil and gas CH4 emissions in AB and SK for the same period.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| 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) | 0.001 | 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".