Mapping methane sources and their emission rates using an aircraft
Bibliographic record
Abstract
Finding efficient and accurate ways to map and monitor methane sources is becoming a priority within government and industry, both for environmental applications and hydrocarbon exploration. For more than 10 years, Sander Geophysics and Shell have cooperated to develop airborne methods to detect and measure the enhanced methane concentrations associated with ground-level sources. The resulting data can be processed using a Markov chain Monte Carlo method (MCMC) to determine the locations and emission rates of the methane sources responsible. SGMethane is the name of Sander Geophysics' methane survey method, resulting from the collaboration with Shell. It consists of an optical gas sensor, an anemometer, a GPS, and an inertial navigation system, analogous to Shell's LightTouch method. A test survey was flown over two active waste landfill sites close to Ottawa, Ontario, Canada, and the modeled data corroborated the locations of the dumpsites. Several commercial surveys for environmental monitoring and hydrocarbon exploration have been flown in a wide variety of different countries and climates; these show that both systems can detect localized anomalous methane sources even in the presence of dense vegetation, such as a tropical rainforest.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".