Addressing key challenges in transportation mode electrification
Bibliographic record
Abstract
Vehicle electrification offers a tremendous potential for greenhouse gas (GHG) reductions in transportation in BC, which account for 24% of total provincial emissions. Battery and drivetrain developments have enabled personal electric vehicles (EVs) to start to penetrate the market, but there remain large opportunities in other parts of the transportation sector. The program of research described in this paper is directed toward exploring these additional opportunities. Bus fleets, and other commercial fleets, present unique challenges and opportunities compared to personal EVs and are being explored in concert with fleet operators. E-bikes enable personal mobility and may also afford health co-benefits that can offer overall financial benefits useful in shaping policy. Comparison between battery-electric and fuel-cell electrified drivetrains is also important to understand the overall round-trip and life-cycle relative efficiencies. These studies are all embedded in a number of modeling frameworks that enable studies of grid-interactions of the vehicles with the grid, in particular taking into account temporally and spatially varying GHG intensities, inter-jurisdictional trading, stochastic planning and operation, and demand response (DR) opportunities. The research is ongoing, so the current paper highlights expected impacts and contributions of the various lines of investigation.
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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 | 0.002 | 0.005 |
| 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 | 0.001 |
| Scholarly communication | 0.004 | 0.008 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.012 | 0.001 |
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".