Blinded evaluation of airborne methane source detection using Bridger Photonics LiDAR
Bibliographic record
Abstract
Controlled, fully-blinded methane releases and ancillary on-site wind measurements were performed during a separate airborne survey of active oil and gas facilities to quantitatively evaluate the capabilities and potential utility of the Bridger Photonics LiDAR-based airborne Gas Mapping LiDAR™ (GML) methane measurement technology under realistic field conditions. Importantly, although Bridger Photonics knew there was a ground team working in the area to deploy wind sensors as part of the broader survey of facilities, they had no knowledge whatsoever that controlled releases were taking place and were not informed of this until all data processing was complete. Thus, the presented data allow a true, fully-blinded assessment of the airborne technology's ability to both detect and locate unknown methane sources within active oil and gas facilities, as well as to quantify their release rates. Results were used to derive a lower-sensitivity limit threshold as a function of wind speed, which matches well with the broader field survey results. Comparison of measurement results with and without the benefit of on-site wind data reveal that uncertainty in the GML source quantification is a direct linear function of the uncertainty in the wind speed. Quantification uncertainties (1σ) of ±31–68% can be expected for sources near the sensitivity limit. The derived sensitivity limit function was incorporated into exploratory simulations using the Fugitive Emissions Abatement Simulation Toolkit (FEAST), which suggest that the Bridger GML technology has comparable performance to optical gas imaging (OGI) camera surveys both in terms of fraction of total emissions detected and anticipated net mitigation. The relative performance of the Bridger GML technology would be expected to improve or worsen as the assumed underlying distribution of source magnitudes becomes more or less positively skewed (i.e. more or less dominated by larger sources such as tank vents). Overall, the Bridger GML technology is shown to be capable of detecting, locating, and quantifying individual sources at or below the magnitudes of recent regulated venting limits. The presented detection sensitivity function will be useful for modelling potential alternate leak detection and repair strategies and interpreting future airborne measurement data.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".