Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| 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 it