Probability of Detection and Multi-Sensor Persistence of Methane Emissions from Coincident Airborne and Satellite Observations
Bibliographic record
Abstract
Satellites are becoming a widely used measurement tool for methane detection and quantification. The landscape of satellite instruments with some methane point-source quantification capabilities is growing. Combining information across available sensor platforms could be pivotal for understanding trends and uncertainties in source-level emissions. However, to effectively combine information across sensors of varying performance levels, the probability of detection (POD) for all instruments must be well characterized, which is time-consuming and costly, especially for satellites. In August 2023, we timed methane-sensing aerial surveys from the Global Airborne Observatory (GAO) to overlap with observations from the NASA Earth Surface Mineral Dust Source Investigation (EMIT). We show how these coincident observations can be used to determine and verify the detection limits of EMIT and to develop and test a multisensor persistence framework. Under favorable conditions, the 90% POD at 3 for EMIT is 1060. We further derive a Bayesian model to infer probabilistically whether nondetected emissions were truly off, and we validate and show how this model can be used to assess the intermittency of emissions with GAO and EMIT. Time-averaged emission rates from persistent sources can be underestimated if POD is not characterized and if differences in POD across multisensor frameworks are not properly accounted for.
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 | 0.005 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".