Investigation of Lithuanian road infrastructure electrification opportunities and constraints.
Bibliographic record
Abstract
This final project analyzed the trends in the use of free electric car charging stations, the latter were installed to promote the growth of the number of electric cars in Lithuania. In the study, each municipality was divided into counties and the growth of the number of electric cars in the regions is reviewed, the data of charging stations is reviewed, thanks to which we can see charging trends. In general, from the date of construction of different charging stations, which varies between 2018 and 2021, to the data recording date of 2022-08-31 for the promotion of e-mobility, State and European projects have significantly contributed to the promotion of electrification and education on Lithuanian roads. 4.5443513 GWhm is exactly how much electricity was used to charge electric cars, it took 390915 charging sessions to generate this number. The number of electric vehicles continues to grow, with the highest growth observed in recent years. From May 2021, the number of electric cars increased by 51% per year. From May 2022 to the end of the first quarter of 2023, the number of electric cars increased by 47.5%. Based on a statistical average, an average of 11.6 kWh was charged per charging session. According to the study: "European Environment Agency. (2019). Monitoring CO2 emissions from passenger cars and vans in 2018", on average a person in Europe drives 40 km per day. That's right, the average cost of one charging session is more than enough energy to drive that distance. The work reviewed the A1 highway, which is one of the main roads in Lithuania and the main communication route between the cities of Vilnius, Kaunas and Klaipėda. Considering the 2023 April 1 The data provided by "REGITRA" and including the goals and prospects for the growth of electric cars until 2030. the number of charging stations is insufficient to serve all the needs of drivers between these cities. There are given recommendations for municipalities on how to prepare and speed up the electrification process.
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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 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 0.000 |
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".