Airborne emissions of methane at offshore oil platforms in Newfoundland and Labrador, Canada
Bibliographic record
Abstract
In Canada, offshore oil production facilities are exempt from new methane mitigation requirements that apply to onshore producers. Since onshore oil and gas operations have been shown in Canada to emit more methane than is reported in the federal inventory, it is reasonable to question methane emission levels, and intensity, of Canada’s offshore oil production. In this study, we measured methane emissions from an aircraft equipped with Picarro 2210-i gas analyzer and Aventech wind measurement system (AIMMs_30). The top-down emission rate retrieval algorithm (TERRA) was used to calculate the emission rate using a mass balance technique. The algorithm was developed by Environment and Climate Change Canada and has been used previously for airborne emissions measurement campaigns around oil and gas facilities. In addition to mass balance estimates, we also derived estimates from downwind transects using a Gaussian Dispersion model. We flew around each of the 3 offshore facilities 3 times to ensure accurate measurements considering the unpredictable offshore weather conditions. Our emissions estimates were overall comparable with inventory estimates, which demonstrate a much lower methane emissions intensity than onshore oil production in western Canada. We compared our results against reported values for other aircraft-based measurement studies including those in the North Sea and the Gulf of Mexico. Although average measured emission rates in Eastern Canada are higher in absolute terms than similar platforms in the Gulf of Mexico or the North Sea, methane emission intensity is lower because production levels are very high. Keyword: Methane emission rate, Inventories, Mass balance, Top-down, Airborne measurement
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".