Bibliographic record
Abstract
Winter in many parts of Canada and the US is an enormous problem for older people. We know that every winter there are many older people who do not get out of their houses for up to three months because they cannot move around safely in the snow, ice, or slushy conditions. This paper describes our efforts at the Toronto Rehabilitation Institute to address the difficulties faced by vulnerable people in winter. The first challenge comes from identifying their perceptions of problems caused by winter. We have subsequently studied the physiologic response to cold and outdoor walking behaviour in wintry conditions in order to understand the problems in greater depth and be able to develop solutions. Emphasis has also been given to investigation of the reported difficulty donning and doffing winter jackets and coats and the effectiveness and safety of winter footwear. Our continuing effort will be focused on the universal design of streetscapes, street furniture, winter clothing, footwear, and improved assistive mobility devices. We are also determining safe exposure levels to cold weather in order to inform the public of the risks associated with mobility in winter and to provide objective criteria for public agency responses to ensure safety and social interventions to reduce isolation in winter.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.032 | 0.006 |
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".