Assessment of Potential Commercial Corridors for Hyperloop Systems
Bibliographic record
Abstract
This study aims at developing a methodology to select and rank the most attractive corridors for the implementation of first commercial vacuum-tube train (or hyperloop) lines for passengers, in complement to traditional tools and methodologies. From a list of the most populated cities all over the world, a first selection of possible transport connections is made, considering that a first commercial vacuum-tube train line has to be viable and safe and therefore cannot require the construction of a tunnel or cross a conflict area. Then, an evaluation of all selected corridors is performed on the basis of defined classification criteria. Important parameters characterizing the potential of a corridor are identified during the research: the number of air passengers on the corridor, the nature of the competitive transport infrastructure, the GDP per kilometre and the topography along the route. Some other minor criteria are also used, in order to elaborate a robust tool which can be a good help for investors and decision makers. All selected corridors are ranked, resulting in a short list of the 250 most attractive corridors for the implementation of first commercial lines. This study presents a proposal for the ranking of the most promising corridors. In order to validate and refine its results, it should be followed by proper feasibility studies on the highest ranked corridors including ridership calculations, sensitivity analyses, etc.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.010 | 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".