GHGSat’s constellation: Land and offshore greenhouse gases detection and quantification 
Bibliographic record
Abstract
GHGSat operates a growing constellation of small satellites tailored for high-resolution imaging and quantification of methane emissions, achieving ~25 m spatial resolution and a sensitivity down to ~100 kg/hr. In 2023, the constellation expanded to 12 satellites with the launch of three additional satellites, including the introduction of the first CO2 sensing instrument. Land operations: We present a comprehensive analysis of the performance across the constellation, demonstrating consistent column precision levels (interquartile range: 1% to 3%) influenced primarily by ground reflectance. To assess the detection threshold, a series of controlled releases were self-organized and performed on a single-blind basis. Fitting our results to a probability-of-detection model we obtain a 50% probability of detection at 3 m/s wind of 102 kg/h. Offshore operations: Detecting and quantifying methane emissions from offshore platforms, which constitutes 30% of oil & gas production, is crucial for providing actionable feedback to industrial operators. Utilizing glint mode for offshore measurements, we capture the direct specular reflection of the sun, enabling quantification of atmospheric methane emissions over water. Our findings reveal a median column precision of 2.1%. Through analytical modeling and orbital simulations, we estimate detection limits ranging from 160 kg/h to 600 kg/h, depending on latitude and season. CO2 satellite: We provide the status of the recently launched CO2 satellite – with the same swath and spatial resolution as our methane satellites. This unit will bring a new dimension to our knowledge of global greenhouses gases emissions. Our presentation underscores the advancements made and insights gained from land and offshore operations, emphasizing the constellation's growing capabilities and the critical role it plays in monitoring and mitigating CH4 and CO2 emissions.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".