Comparing satellite methane measurements to inventory estimates: A Canadian case study
Bibliographic record
Abstract
Methane emissions from natural gas production are of increasing importance as they threaten efforts to mitigate climate change. Current inventory estimates carry high uncertainties due to difficulties in measuring emission sources across large regions. Satellite measurements of atmospheric methane could provide new understanding of emissions. This paper provides insight into the effectiveness of using satellite data to inform and improve methane inventories for natural gas activities. TROPOMI data are used to quantify methane emissions from natural gas within the Montney basin region of Canada and results are compared with existing inventories. Emissions estimated using TROPOMI data were 2.6 ± 2.2 kt/day which is 7.4 ± 6.4 times the inventory estimates. Pixels (7 by 7 km) that contained gas facilities had on average 11 ppb more methane than the background. 7.4% of pixels containing gas sites displayed consistently high methane levels that were not reflected in the inventory. The satellite data were not sufficiently granular to correlate with inventories on a facility scale. This illustrates the spatial limitations of using satellite data to corroborate bottom-up inventories.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".