Guest Editorial. Special Section on Design, Modeling, and Control of Hybrid and Multi-source Vehicles
Bibliographic record
Abstract
Advanced traction and propulsion systems are developed in order to ensure better energy performance, reduced operating cost, and higher lifetime of future transportation systems like road vehicles, but also more electric trains, subways, ships and airplanes. Such a powertrain integrates several complex subsystems (including power or energy sources, electric machines, power electronics, mechanical transmission) and it becomes mandatory to consider the whole system in order to reach the best performance. IEEE Vehicle Power and Propulsion Conference (VPPC) is an annual conference of the IEEE Vehicular Technology Society (VTS). The 12th VPPC was held in Montreal, Quebec, Canada, in October 2015. There were 226 papers submitted and 170 papers accepted and presented at the conference. In order to further promote research excellence in vehicle power and propulsion, in collaboration with the 2015 IEEE VPPC team, a Special Section of the IEEE Transactions on Vehicular Technology has been organized to focus on state-of-the-art research and development as well as future trends in modeling, design, and control of advanced power and propulsion systems for modern transportation systems.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.051 | 0.029 |
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".