Guest Editorial Introduction to the Special Section on Innovative Electrified Vehicles
Bibliographic record
Abstract
The papers in this special section focus on innovative electrified vehicles. According to the “Global EV Outlook 2021” of International Energy Agency (IEA), ten million electric cars were on the world’s roads in 2020. It was an important year for the electrification of mass market transportation with the share of 4.6% of total car sales around the world, despite of Covid-19 pandemic and its related economic downturn and lockdowns. Nevertheless, the challenge remains enormous to reach the target of 230 million EVs on the world’s roads by 2030, according to the IEA Sustainable Development Scenario. In addition to technologies related to road vehicles, the electrification of railway vehicles, air and water vehicles also requires fast development to combat the global climate change. Papers included in this special section were presented at the IEEE Vehicle Power and Propulsion Conference (VPPC).
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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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.008 | 0.010 |
| Insufficient payload (model declined to judge) | 0.033 | 0.023 |
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".