Engineering Poster E4: Strength in numbers: how the different satellite systems used to monitor methane emissions from space have different, yet complementary, capabilities to help the oil and gas industry meet its decarbonisation goals
Bibliographic record
Abstract
Poster E4 Satellites are a powerful tool in monitoring methane emissions around the world. In the last 5 years, many new systems have been both announced and deployed, each with different capabilities, and designed for a specific purpose. With an increase in options also comes confusion as to how these systems can and should be used. This paper will examine how these systems complement each other’s strengths and weaknesses to provide actionable insight to the oil and gas industry. The performance parameters of several current and future satellite systems will be presented and compared, supported with recent examples when available. The importance of factors like frequency of revisit, detection threshold, precision, and spatial resolution will be discussed and contrasted with the needs of the oil and gas industry in gaining a more complete understanding of its methane emissions, in providing key information to stakeholders, and in enabling action to mitigate emissions. Results from GHGSat’s second generation of high-resolution satellites displaying measurements of methane plumes at oil and gas facilities around the world will be presented to demonstrate some of the advantages of the technology. These two satellites, GHGSat-C1 and C2 (Iris and Hugo), were launched in September 2020 and January 2021, respectively. Another eight satellites are planned to be launched by mid-2023. Finally, the ability of these systems to work together and complement each other’s capabilities, and some of the analytics tools used to augment the data, will be presented. To access the poster click the link on the right. To read the full paper click here
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".