Single-blind inter-comparison of methane detection technologies – results from the Stanford/EDF Mobile Monitoring Challenge
Bibliographic record
Abstract
Methane leakage regulations in the US and Canada have spurred the development of new technologies that promise faster and cheaper leak detection for the oil and natural gas industry. Here, we report results from the Stanford/EDF Mobile Monitoring Challenge – the first independent assessment of 10 vehicle-, drone-, and plane-based mobile leak detection technologies. Using single-blind controlled release tests at two locations, we analyze the ability of mobile technologies to detect, localize, and quantify methane emissions. We find that the technologies are generally effective at detecting leaks, with 6 of the 10 technologies correctly detecting over 90% of test scenarios (true positive plus true negative rate). All technologies demonstrated pad-level localization of leaks, while 6 of the 10 technologies could assign a leak to the specific piece of equipment in at least 50% of test scenarios. All systems tested here will require secondary inspection to identify leak locations for repair; thus, mobile leak detection technologies can act as a complement, and not a substitute, for currently used optical gas imaging systems. In general, emissions quantification needs improvement as most technologies were only able to generally provide order of magnitude emissions estimates. Improvements to quantification algorithms, reducing false positive detection rates, and identifying early applications will be critical for deployment at scale. Even as this study provides the first independent verification of the performance of mobile technologies, it only represents the first step in the road to demonstrating that these technologies will provide emissions reductions that are equivalent to existing regulatory approaches.
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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 | 0.007 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".