Meter-scale retrieval of industrial methane emissions using GHGSat’s satellite constellation
Bibliographic record
Abstract
Actionable feedback to industrial operators is extremely valuable to help them reduce their greenhouse gas emissions. With this goal in mind, GHGSat launched in 2016 a demonstration satellite called GHGSat-D (“Claire”). It was the first satellite built specifically to detect and quantify methane emissions from individual sites. With the launches of GHGSat-C1 (“Iris”) in September 2020 and of GHGSat-C2 (“Hugo”) planned in January 2021, GHGSat will have three methane-sensing meter-scale resolution satellites in orbit. In addition to those satellites, GHGSat has also deployed an aircraft version of the instrument to survey specific areas with even lower detection threshold thanks to its higher spatial resolution. This presentation will show the improvements done since GHGSat-D that allow our instruments to reach column precision of 1% of background. With this enhanced sensitivity, sources such as oil and gas facilities, mines, landfills and dams can be measured from space. Emission quantification of various sources will be presented and will demonstrate that GHGSat-C1 is approaching its target detection threshold of 100 kg/h. We will also illustrate the complementarity of GHGSat’s instruments with Sentinel-5P, the first ones able to detect individual sources with low emission rates, the second able to measure daily and with high accuracy global methane concentrations. We will also discuss the data calibration and validation plan of our instruments. Finally, an update on the future expansion of GHGSat’s constellation will be given.
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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 | 0.000 | 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".