Design of built environments to accommodate mobility scooter users: part II
Bibliographic record
Abstract
PURPOSE.Accessibility standards for wheeled mobility devices currently use a 1.5 m turning circle, designed to accommodate manual wheelchairs. Scooters are less manoeuvrable than wheelchairs, so allowing a full turning circle would require too much space. Instead, we propose using a rectangle that provides space for a three-point turn. Here, we determine the area requirements of this approach. METHOD. For rectangular 'rooms' of varying aspect ratios, we measured the minimum dimensions in which two four-wheeled scooters (the Celebrity-X and Fortress-1700), which combine good outdoor performance with reasonable indoor manoeuvrability, could enter the space, perform a three-point turn and exit. Moveable Styrofoam walls defined each 'room', and a doorway was located either near the corner of the space or in the middle of one wall. 'Room' size was decreased until our expert driver could no longer perform the manoeuvre. RESULTS. Compared to the area required for a turning circle, 42-54% savings were achieved. Relative to existing requirements, 53-95% more space is required to accommodate the Celebrity-X; 173-223% increases are necessary for the Fortress-1700. CONCLUSIONS. When accommodating four-wheeled scooters, our proposed three-point turn definition would require more space than the current standards, but considerably less than if a full turning circle were used.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 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".