The Potential Impact of Biodiesel Under a Scenario of Increased
Bibliographic record
Abstract
The capabilities of the Canadian Transportation Energy and Emissions Model (CanTEEM) have been enhanced to permit the investigation of an enriched set of scenarios and fuel pathways for road transportation in Canada. In this study, the impact of the introduction of advanced diesel engines into 30 % of the light duty vehicle stock, and the combined effect of the use of biodiesel blended fuel in the road transportation sector are simulated. The potential impact on GHG emissions due to biodiesel is expected to be small compared to the reduction arising from the penetration of advanced diesel engines. The potential environmental benefits are being limited by the cold flow properties of biodiesel and the limited supply of economical feedstocks in Canada. While GHG emissions are expected to be reduced in the short term, the growth in population and economic activities will likely dominate in the longer term. From a policy standpoint, this highlights the importance of policies that promote higher fuel efficiency for the remaining 70 % of the light duty vehicle stock – including the use of conventional gasoline engines, and other advanced technologies such as
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How this classification was reachedexpand
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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".