Étude de faisabilité préliminaire de la technologie de transport par tube
Bibliographic record
Abstract
The Innovation Centre of Transport Canada commissioned the “Preliminary Feasibility of Hyperloop Technology” study that was conducted by AECOM Canada to appraise and review two principal considerations:Can the Hyperloop concept be transformed into a viable technology that is safe for passengers and communities? Is the Hyperloop technology cost significantly more affordable than, or at least comparable to conventional High-Speed Rail systems or developing Maglev technologies? To support the findings: a literature review was conducted; an engineering assessment was performed; an evaluation of projected capital and operating costs was done; and a risk and hazard assessment was prepared. A selection of Hyperloop technology providers participated in the study by responding to AECOM solicited surveys to provide essential information on engineering, design, operational characteristics, and capital and operating cost assumptions. Based on the evidence gathered: Hyperloop has not yet been fully conceptualized. Many of the questions investigated could not be answered because the technology is not sufficiently mature or doesn’t offer sufficient information/design options. Several technical components are in the very early stages of development and likely years away from functional realization. As a result, it is difficult to determine if Hyperloop will become a viable mode of transportation. Although uncertainty remains over Hyperloop’s viability, this appraisal recognizes that the technology has advanced rapidly since first proposed in 2013. Many challenges considered insurmountable only a few years ago have been overcome giving cause for optimism. However in its current state, Hyperloop is unlikely to be ready for real-world application in the near future. Hyperloop infrastructure costs are expected to be significant and user costs are anticipated to be more likely comparable to airfares than other land-based transportation options. This would lead to the conclusion that, should Hyperloop be commercially launched, it will most likely start as an inter-city/metropolitan service.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.055 | 0.216 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.018 | 0.023 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.018 | 0.010 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".