Assessment of Potential Commercial Corridors for Hyperloop Systems
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.<br/>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.<br/>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.<br/>All selected corridors are ranked, resulting in a short list of the 250 most attractive corridors for the implementation of first commercial lines.<br/>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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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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 it