US oil and gas system emissions from nearly one million aerial site measurements
Bibliographic record
Abstract
Abstract Aerial methane surveys of oil and natural gas systems have discovered large emissions, which are missing or vastly undercounted in official estimates. We integrate approximately one million aerial site measurements into regional emissions inventories, employing empirically-grounded emissions simulations to estimate small emissions. We infer emissions inventories for six regions in the United States comprising 52% of onshore oil and 29% of gas production over fifteen aerial campaigns. Total estimated emissions range from 9.63% [9.03%, 10.37%] of natural gas production, roughly nine times the US government estimate, to 0.75% [0.65%, 0.85%] in a high-productivity gas-rich region. Aerially measured emissions at 0.05%-1.44% of well sites contribute 50%-81% of total emissions in twelve of fifteen campaigns. The social cost of methane emissions from these measured regions is roughly $9.4 billion per year, in addition to roughly $1 billion in lost sales. This highlights the importance of incorporating comprehensive remote sensing surveys into emissions inventories and efforts to benchmark and reduce methane 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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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".