Modelling the Effects of E10 Fuels in Canada
Bibliographic record
Abstract
In May 2003, Health Canada held an expert panel on ethanol-blended gasoline and how its widespread use might affect human health risks from exposure to vehicle exhaust pollutants in Canada. One of the key topics of discussion was the availability of existing atmospheric photochemistry and air quality modelling results. Most of the published information was embedded in more general studies of reformulated gasoline in the U.S. and is not directly applicable to Canada because of differences in fuel formulation, vehicle fleet, and climatic conditions. Based on this information gap, the authors have undertaken a modelling exercise to quantify the effects of E10 (10 % ethanol blend gasoline) splash and tailor blended fuels on the formation of smog and air toxics. Modelling is being performed over two model domains (eastern North America and the Pacific Northwest) covering two meteorological episodes for different base year emission inventories (2000 and 2010). An integral part of the emission processing has included the use of the recently ‘Canadianized ’ version of the MOBILE emission model and a modified version of the SMOKE emission processor that is capable of handling toxic species (specifically benzene and 1,3-butadiene) explicitly using a modified version of the
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".