Monitoring of Greenhouse Gas Emissions From Space in the Middle East
Bibliographic record
Abstract
Abstract In June 2016, GHGSat launched the world's first satellite capable of measuring greenhouse gas emissions from targeted industrial facilities around the world. We offer a single solution to measure emission rates of carbon dioxide and methane from selected targets with greater precision and lower cost than ground-based alternatives, across a wide range of industries. GHGSat is deriving the emission rates of these sources from 12 × 12 km maps of the atmospheric column densities of carbon dioxide and methane produced using its patented sensor at a spatial resolution better than 50 m. Satellite mass is less than 15 kg. Our solution provides industrial site operators and government regulators with the information they need to understand and manage their greenhouse gas emissions better and ultimately to reduce them more economically. We will describe the system, including the sensor and satellite specifications. We will also describe our products and services, show how they apply to the oil and gas industry in the Middle East and provide examples of various levels of imagery taken from the region with our satellite.
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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 | 0.002 | 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 | 0.000 |
| 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".