Bibliographic record
Abstract
Some municipalities cannot succeed at making its citizens use public transportation. The use of private cars is usually preferred and this leads to more congestion, longer commuting time, more fuel consumption and gasses’ emissions. Travel preferences of commuters are commonly estimated with discrete choice methods that consider their socioeconomic characteristics, along with some form of travel cost, failing to incorporate any measure of comfort. This research develops a standardized indicator of comfort for mass transportation systems. The functional form for a proposed index is developed over three key indicators: vehicle vibrations, air quality and noise levels, and the index is illustrated on a case study of the city of Montreal with comparisons to London and Santo Domingo (Dominican Republic). The index was developed in a way that allows an objective calculation, avoiding qualitative judgment from commuters, thus eliminating individuals’ subjectivity, and enabling comparisons among cities and modes. It was found that the automobile is the most comfortable mode, explaining its popularity. The data showed that, the number of stops is the most important factor affecting total vibration levels, and hence the comfort of buses and trains. Noise was found to be linked to vehicle’s vibrations. Newer metro cars in London and Dominican Republic showed better comfort levels, suburban trains in Montreal performed better and close to their counterparts in the United Kingdom. Express bus line was more comfortable than the local bus, performing better in the level of vibrations and noise, but not in terms of air quality.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".