IEEE Transactions On Vehicular Technology, Guest Editorial : Special Section on Advanced Transportation Systems
Bibliographic record
Abstract
ADVANCED electric drive traction systems are being developed in order to ensure better energy efficiency for the emerging and future transportation systems such as electric vehicles (EVs), hybrid electric vehicles (HEVs), plug-in hybrid electric vehicles (PHEVs), fuel cell vehicles (FCVs), as well as electrified and advanced tractions and propulsions for trains, subways, ships, and airplanes. The research and development aims at reducing energy consumption and pollutant emissions in order to improve sustainability and address climate change. In this special section, The three first papers deal with new electric drives (a new electromagnetic active suspension, an electric variable transmission, and a charger-traction drive) where the design is realized by taking into account the constraints of the entire vehicle system. Two papers present the energy management of complex hybrid energy storage subsystems (fuel cell/supercapacitor and fuel-cell/battery/supercapacitor) using a new 'system description' [Energetic Macroscopic Representation (EMR)]. Two papers propose statistical studies of real-life drive cycles for the design and analysis of new low-carbon vehicles (plug-in hybrid electric vehicles and fuel cell vehicles).
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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) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.031 | 0.026 |
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".