Forecast of the development of demand for charging points for electric vehicles in Russian cities
Bibliographic record
Abstract
Road transport is one of the main consumers of fossil fuels in the world. At the same time, ICE vehicle emissions form about a quarter of all greenhouse gases in the world. In this regard, the transition to electric vehicles is extremely important. Their operation is more economical and environmentally friendly. The massive shift away from liquid-fuel vehicles will have a significant positive effect on a global scale. At the moment, more than 20 countries of the world have planned the transition to electric vehicles by 2030-2040. They are beginning to be introduced into various areas: personal and public transport, special-purpose vehicles, cargo transportation, etc. At the same time, there are barriers that need to be overcome for the widespread mass transition to electric cars. In particular, the choice of models is limited and their price is high. The technologies for the production and disposal of individual components, such as batteries, remain insufficiently developed. Electric networks are not always ready for additional load. The network of electric filling stations is not sufficiently developed. Russia also supports this global trend and faces restrictions. Various scenarios for the development of electric vehicles have been predicted. Measures of state support for both consumers and manufacturers of electric vehicles are indicated, which are aimed at achieving the indicators of a balanced or accelerated scenario for this market.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".