Bibliographic record
Abstract
There is an urgent need to identify adaptation strategies that minimize the risks of extreme heat events, especially among vulnerable groups such as older adults with low socioeconomic status. Heat-health research has focused on outdoor temperatures; however, heat-related mortality occurs almost exclusively at home. This project proposes a shift towards monitoring the daily physiological responses and behaviors of older adults living in social housing across a wide range of indoor environmental conditions to which they are exposed during the summer months. The aim of the HeatIN project is to identify indoor temperature and humidity thresholds at which physiological and behavioral responses become altered. The results will allow us to determine the indoor environment at which heat mitigation strategies should be implemented to protect health and well-being. Ultimately, this project will lead to more targeted heat action planning, which will contribute to climate change adaptation. The HeatIN project has received funding from Health Canada under the HeatAdapt program.
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.
How this classification was reachedexpand
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 | 0.010 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.002 | 0.025 |
| Science and technology studies | 0.003 | 0.011 |
| Scholarly communication | 0.013 | 0.002 |
| Open science | 0.048 | 0.020 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.273 | 0.272 |
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; both teacher heads agree on what is shown here.
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".