Bibliographic record
Abstract
Reduced pricing, range extension, quiet and luxurious ride experience, sleek designs, and most importantly ‘zero emission’ are some of the promotional features electric vehicle (EV) sellers use to entice potential buyers. But one thing these sellers cannot yet convince the shoppers is about the EV charging access and convenience. As the EV market share continues to grow, a common goal of all the regulatory entities, it is critical to understand the status of the charging infrastructure. This article depicts the latest status of EV charging infrastructure as of the year 2023–2024 and the potential needs for expanding the same using data-driven calculations and various global targets set for 2030 and beyond. It takes a fresh look at the reality of the existing charging stations and identifies the key areas of improvement to address common EV user concerns. Using a holistic approach of well-to-wheels investigation, the potential improvements discussed here benefit a range of stakeholders within the e-mobility ecosystem, rather than concentrating on just the charging station. With the intent of making the discussion more resourceful for both investors and technologists, data is presented using various public sector sources apart from the overviews on advanced technology concepts that can enhance e-mobility adoption.
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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.007 | 0.011 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".