A demonstration of route planning methodology for vehicle-based measurements of methane and other gas emissions from point sources.
Bibliographic record
Abstract
Multi-sensor vehicle systems have been implemented in large-scale field programs to detect, attribute, and estimate emissions rates of methane (CH4) and other compounds from oil and gas wells and facilities for research and applied purposes. Most vehicle systems use passive sensing, so they must be positioned downwind of sources to detect enhancements. A major deployment challenge for regional surveys is predicting the best measurement locations and driving routes. Here we present a methodology incorporates high-resolution weather forecast and geospatial data to predict measurement locations and optimize driving routes. The methodology estimates the downwind road intersection point (DRIP) of theoretical CH<sub>4</sub> plumes emitted from each well or facility. DRIPs serve as waypoints for Dijkstra’s shortest path algorithm to determine the optimal driving route. Although development of the methodology was motivated by CH<sub>4</sub> emissions from oil and gas facilities, it should be applicable to other types of air emissions and contexts involving passive sensing from vehicles. This video demonstrates a fictitious concentration survey of seven restaurants in Coquitlam, BC, Canada.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.104 | 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 teacher head, 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".