Data from: Global energy sector methane emissions estimated by using facility-level satellite observations
Bibliographic record
Abstract
Methane emissions from energy sector facilities (oil, gas, and coal) represent a 15% significant contribution to global greenhouse gas emissions with substantial mitigation potential. We estimate global 2023 methane emissions from energy sector point-sources using the high spatial resolution GHGSat satellite constellation. GHGSat detected 8.30 ± 0.24 Mt yr-1 of methane emissions from 3,114 emission sites. Detected O&G and coal emitting sites are found to be emitting 16% and 48% of the time, respectively, above GHGSat’s detection limit without 20 significant continental variations. Compared to the Global Fuel Exploitation Inventory (GFEIv3), GHGSat’s estimate comprises 12% of GFEIv3’s total emissions, or 24% over GHGSat-observed locations, with good spatial correlation at the country scale but only weak spatial correlation at the 0.2 ° x 0.2 ° grid cell scale.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.025 | 0.030 |
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".