Accuracy of satellite-derived estimates of flaring volume for offshore oil and gas operations in nine countries
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract Flaring of natural gas contributes to climate change and wastes a potentially valuable energy resource. Various groups have estimated flaring volumes via remote sensing by nighttime detection of flares using multi-spectral imaging. However, only limited efforts have been made to independently assess the accuracy of these estimation methods. I analyze the accuracy of the VIIRS Nightfire published flare detection results, comparing yearly estimated flaring rates to reported flaring data from governments in 9 countries (Brazil, Canada, Denmark, Mexico, Netherlands, Nigeria, Norway, USA, UK) and 7 years (2012–2018 inclusive). We analyze only flares occurring at offshore oil and gas production platforms and floating production units. A total of 1054 flare volume estimates were compared to volumes reported to government agencies. 80.8% of flare estimates lie within 0.5 orders of magnitude (OM) of reported volumes, which 93.7% fall within 1 OM of the reported volume. Little systematic bias is found except in the smallest size classes (<10 6 m 3 y −1 ). Relative error ratios are larger for smaller flares. No significant trend was observed across years, and variation by country is in line with that expected by size distribution of flares by country. Wide aggregate estimates for groups of flares will exhibit little bias and dispersion, with the sum of 1000 flares having an expected interquartile range of −6% to +3% of actual reported volumes. Social media blurb: Test of remote sensing for flare detection shows accuracy across 9 countries and 8 years.
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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 | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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 it