Emissions from the Canadian oil sands:  Merging aircraft and satellite observations to derive emissions of pollutants co-emitted with NOx
Bibliographic record
Abstract
The Athabasca Oil Sands Region (AOSR) in Alberta, Canada is one of the largest sources of extractable oil in the world. To better understand its impact, Environment and Climate Change Canada led two intensive measurement campaigns, in 2013 (August to September) and 2018 (April to July). Each included airborne measurements in which dozens of species were measured using a variety of in situ instruments. In this presentation, a method is described in which these aircraft measurements were examined to find species that were well correlated with NOx (the sum of NO and NO2) in order to derive their annual emissions. The species found to have a good correlation with NOx were black carbon, CO, HCN, HONO, CH4, and SO2. The annual emissions were found by applying individual species to NOx ratios to the satellite-derived NOx emissions from the TROPOspheric Monitoring Instrument (TROPOMI). The emissions derived in this way were compared with emissions reported to the National Pollutant Release Inventory (NPRI), as well as emissions derived from the aircraft measurements using the Top-down Emission Rate Retrieval Algorithm (TERRA). Additionally, Ozone Monitoring Instrument (OMI) NOx emissions were used to estimate historical changes in species emissions over time, between 2005 and 2020.
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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 | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".