Investigation\nof the Spatial Distribution of Methane\nSources in the Greater Toronto Area Using Mobile Gas Monitoring Systems
Bibliographic record
Abstract
For\nmethane emission reduction strategies in urban areas to be\neffective, large emitters must be identified. Recent studies in U.S.\ncities have highlighted the contribution of methane emissions from\nnatural gas distribution networks and end use. We present a methane\nemission source identification and quantification method for the Greater\nToronto Area (GTA), the largest metropolitan area in Canada, using\nmobile gas monitoring systems. From May 2018 to August 2019, we collected\n77 surveys of methane mixing ratios, covering a distance of about\n6400 km, and sampled emission plumes from sources such as closed landfills,\nnatural gas compressor stations, and waterways. Our results indicate\nthat inactive landfills emit less than inventory estimates. Despite\nthis discrepancy, we confirm that the waste sector is the largest\nmethane emitter in the GTA. We also report that the frequency of methane\nleaks from the local distribution system ranges between 4 and 22 leaks\nper 100 km of roadway in downtown Toronto, which is comparable to\nthe range observed in U.S. cities, which have invested in modern natural\ngas distribution infrastructure. Last, we find that engineered waterways,\nwhose emissions are currently not reported in inventories, may be\na significant source of methane.
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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 | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".