Assessment of GHGSat’s constellation one year after its first phase deployment
Bibliographic record
Abstract
Methane emissions from industrial activities represent a significant fraction of total greenhouse gas emissions. It is vital to provide industrial site operators with accurate and timely information about their emissions -- GHGSat’s constellation was built for this purpose. Each of the constellation satellites can do multiple measurements of 150 km2 domains each day with a pixel resolution of 25 meters allowing to detect, quantify and attribute emissions to a given facility. With 3 satellites currently in operation, this allows multiple measurements of a site in a year and help operators minimize their emissions. We will present the performance of our instruments showing a column precision of 1% of background and a detection threshold of 100 kg/h for point sources. Examples from a variety of anthropogenic sources will illustrate the system capability. A statistical analysis of all detected emissions will serve to evaluate the distribution of global source rates, source intermittency and breakdowns by region and by sector. An estimate of emissions mitigated thanks to GHGSat’s constellation will also be presented. Finally, the schedule of the next phases of the constellation will be outlined.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".