Design of built environments to accommodate mobility scooter users: part I
Bibliographic record
Abstract
PURPOSE: To determine the minimum dimensions needed to allow five models of powered mobility scooters to manoeuvre within five commonly encountered indoor spatial configurations. METHOD: We measured manoeuvrability of five scooters judged by their manufacturers to have a good combination of indoor mobility and outdoor performance (including in rural environments). We determined the minimum space needed to manoeuvre the scooters through the following five spatial configurations: turning 180° in a corridor, performing U-turns around 50 mm (2″) and 1200 mm (4') obstacles, turning 90° from a doorway and approaching a counter or work surface from the side. Free-standing styrofoam walls were used to define each configuration. An expert driver repeatedly manoeuvred the scooters through each configuration while we incrementally decreased the dimension of interest until it was no longer possible to complete the manoeuvre. Each scooter's turning diameter was also measured and compared to the manufacturer's specification. RESULTS: Minimum space requirements for each scooter for five spatial configurations are given and compared to existing standards. CONCLUSIONS: None of the scooters tested were capable of completing all manoeuvres within the space allowed by existing standards. These findings will contribute to recommendations for new standards for built environments that can accommodate scooter users.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".