Bibliographic record
Abstract
Abstract. Mobile real-time measurements of ambient methane provide a fast and effective method to identify and quantify methane leaks from local gas distribution systems in urban areas. The objectives of these methodologies are to i) identify leak locations for repair and ii) construct measurement-based emission rate estimates, which can improve emissions reporting and contribute to monitoring emission changes over time. Currently, the most common method for emission quantification uses the maximum methane enhancement detected while crossing a methane plume. However, the recorded maximum depends on instrument characteristics, such as measurement cell size, pump speed and measurement frequency. Consequently, the current approach can only be used by instruments with similar characteristics. We suggest that the integrated spatial peak area is a more suitable quantity that can eliminate the bias between different instruments. Based on controlled release experiments conducted in four cities (London, Toronto, Rotterdam, and Utrecht), emission estimation methodologies were evaluated. Indeed, the integrated spatial peak area was found to be a more robust metric across different methane gas analyzer devices than the maximum methane enhancement. A statistical function based on integrated spatial peak area is proposed for more consistent emission estimations when using different instruments. On top of this systematic relation between actual emission rate and recorded spatial peak area, large variations in methane spatial peak area were observed for the multiple transects across the same release point, in line with previous experiments. This variability is the main contributor of uncertainty in efforts to use mobile measurements to prioritize leak repair. We show that repeated transects can reduce this uncertainty and improve the categorization into different leak categories. We recommend a minimum of three and an optimal range of 5-7 plume transects for effective emission quantification to prioritize repair actions.
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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 | 0.003 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.030 | 0.027 |
| Insufficient payload (model declined to judge) | 0.144 | 0.121 |
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".