Dieselisation of the Light-Duty Vehicle Fleet in Canada
Bibliographic record
Abstract
CanTEEM has been upgraded to facilitate the investigation of the potential impacts resulting from increased oil sands production. A case study is conducted based on scenarios representing the projected rise in production of bitumen and synthetic crude oil, along with the rise in fuel consumption for road transportation due to growth in population and the economy. While the estimated impacts on Canadian GHG emissions and natural gas use present no real surprises, the simulation helps to quantify the potential implications and to identify those areas where concerted effort will be needed to mitigate these impacts. Oil sands development based on current trends in production and technologies will counter measures for tackling climate change, and is not sustainable in the long term. Whereas economic considerations may point to increasing the upgrading of in-situ bitumen production, the accompanying side effects need to be properly understood and quantified as well. Assuming heavier residual oil sands products will be used to supply energy, the severe demand on natural gas could be eased and delay somewhat the future point of gas shortage, yet at the cost of even
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".