Study of the Space Occupied by a Wheelchair User at Metro de Santiago Platforms by Laboratory Experiments
Bibliographic record
Abstract
The objective is to study the space a wheelchair passenger would use in the preferential waiting areas of an urban railway platform. For this purpose, an analysis of the variables that interfere in the space occupied by a wheelchair user was carried out at Metro de Santiago platforms, to design a preferential prototype waiting area at the Human Dynamics Laboratory of the Universidad de los Andes (Chile). The prototype has a 3.0 m long and 2.5 m wide carriage, together with a preferential waiting area on the platform of the same length as the carriage, and 2.0 m wide. With this prototype defined, a detection method was developed using PeTrack software, to accurately identify the limits of the space occupied by a wheelchair user under two scenarios of different density levels in the preferential waiting area: medium-density (1.5 passengers/m2) and high-density (4.0 passengers/m2). It was observed that the space occupied by a wheelchair user decreased by 33% as the density increased from medium to high. On the other hand, for a high-density level, the space occupied by a wheelchair user was found to be 61% higher than that occupied by a passenger without reduced mobility. This variation occurred mainly because passengers increased their distance from the wheelchair user, which widened their area of influence. Further experiments are proposed as future research to extend this analysis using other density situations.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".