Quantifying Mobility Scooter Performance in Winter Environments
Bibliographic record
Abstract
OBJECTIVES: To quantify mobility scooter performance when traversing snow, ice, and concrete in cold temperatures and to explore possible performance improvements with scooter winter tires. DESIGN: Cross-sectional. SETTING: Hospital-based research institute. PARTICIPANTS: Two drivers (50 and 100 kg) tested 8 scooter models (N=8). Two mobility scooters were used for winter tire testing. INTERVENTIONS: Scooters were tested on 3 different conditions in a random sequence (concrete, 2.5-cm depth snow, bare ice). Ramp ascent and descent, as well as right-angle cornering up to a maximum of 10° slopes on winter conditions, were observed. Winter tire testing used the same slopes with 2 scooters on bare and melting ice surfaces. MAIN OUTCOME MEASURES: Maximum achievable angle (MAA) and tire traction loss for ramp ascent and descent performance. The ability to steer around a corner on the ramp. RESULTS: [2, N=8]=12.25, P<.01). Half of the scooters we tested were unable to climb a 1:12 grade (4.8°) snow-covered slope without losing traction. All but 1 failed to ascend an ice-covered 1:12 grade (4.8°) slope. Performance was even more unsatisfactory for the forward downslopes on both snow and ice. Winter tires enhanced the MAA, permitting 1:12 (4.8°) slope ascent on ice. CONCLUSIONS: Mobility scooters need to be designed with winter months in mind. Our findings showed that Americans with Disabilities Act-compliant built environments, such as curb ramps that conform to a 1:12 (4.8°) slope, become treacherous or impassible to mobility scooter users when covered in ice or snow. Scooter manufacturers should consider providing winter tires as optional accessories in regions that experience ice and snow accumulation. Additional testing/standards need to be established to evaluate winter mobility scooter performance further.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".