Transport Service Electrification in Developing Countries
Bibliographic record
Abstract
Considering the new stringent constraints proposed by public authorities, decarbonization became a key trend in the last years. Transport sector still represents one of the most pollutant fragment. Even if several countries start their process of decarbonization through the introduction of several Electric Vehicles in their public services, still, for several countries, especially the developing ones, transportation represents a hard to abate sector, which generally uses outdated and pollutant vehicles, discouraging the use of public transport and facilitating the creation of traffic congestions. Basing on these considerations, this work want to implement a simulation for a public service in a developing country, evaluating if it is possible to perform a long trip using an electric minibus. Therefore, a case study will be implemented highlighting the barriers of the transport electrification in this area, producing results on consumption and reliability of the service. Finally, an environmentally sustainable solution to power the service will be proposed to highlight the potential of electrification in the area.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".