A Simplified Heat Wave Warning System
Bibliographic record
Abstract
Abstract \nA Simplified Heat Wave Warning System \nArya Nakhaie Ashtiani \nExtreme heat is a natural hazard that could rapidly increase in frequency, duration, and magnitude in the 21st century. During the summer, the combined effect of urban heat island (UHI), climate change and global warming increases ambient air temperature. This leads to a rise in indoor environment temperature, reduction of thermal comfort, increase of cooling demand, and heat related morbidity and mortality especially among vulnerable people such as the elderly and those who are living in buildings without mechanical ventilation systems. \nCities are developing tools to predict the indoor air temperature during extreme heat waves in order to be able to provide emergency plans if necessary. To do so, it is required to find a relationship between the indoor and outdoor conditions. Hence there is an urgent need to develop a reliable method for indoor air temperature prediction by taking into consideration not only the outdoor conditions but also the socio-economic aspects of the neighborhood. \nThe objective of this study is to develop a warning system to predict the indoor air thermal condition during heat wave events in buildings without mechanical ventilation systems. In order to develop a regional heat warning system, two different methods were proposed and tested for an indoor air temperature forecasting application with respect to neighborhood parameters. The first method was based on regression and the second one was based on the Artificial Neural Network (ANN) model. The inputs and outputs to the proposed models were the field measurement data which has been collected on Montreal Island during the summer of 2010 (Park et al, 2010). To find the most practical approach, both proposed models were compared with respect to their accuracy and the required resources. A comparison of the proposed regression and ANN models was conducted by two different levels of simulation. The ANN model showed better accuracy in predicting the indoor dry-bulb temperature, but it was more complicated to apply.
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 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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".