Overheating Risk Analysis in Long-Term Care Homes—Development of Overheating Limit Criteria
Bibliographic record
Abstract
Climate heat waves occurring in urban centers are a serious threat to public health and wellbeing. Historically, most heat-related mortalities have arisen from excessive overheating of building interiors housing older occupants. This paper developed an approach that combines the results from building simulation and bioheat models to generate health-based limit criteria for overheating in long-term care homes (LTCHs) by which the body dehydration and core temperature of older residents are capped during overheating events. The models of the LTCHs were created for buildings representative of old and current construction practices for selected Canadian locations. The models were calibrated using measurements of indoor temperature and humidity acquired from monitoring the building interiors and the use of published building energy use intensity data. A general procedure to identify overheating events and quantify their attributes in terms of duration, intensity, and severity was developed and applied to LTCHs to generate the limit criteria. Comparing the limit criteria from the proposed and comfort-based methods showed evident differences. The proposed method predicted the overheating risk consistent with the overall thermal comfort during overheating events in contrast to the comfort-based methods. The new limit criteria are intended to be used in any study to evaluate overheating risk in similar buildings.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".