Analysis of window opening in arctic community housing and development of data-driven models
Bibliographic record
Abstract
Achieving low-energy, comfortable buildings in remote Arctic regions like Nunavik, Canada, presents multiple challenges due to high heating demand and limited energy supply. Understanding human behavior and the impact of practices on energy use is a key element in improving building performance. While previous research on building performance in Arctic regions has often adopted a building-centered approach, this study offers a complementary approach by investigating occupant behavior, focusing on window openings. The sample includes 10 monitored houses in the village of Quaqtaq, Nunavik. Data on weather conditions and window and door status were collected from September 2018 to August 2020. Behavior trends were identified using metrics such as the opening ratio, median duration of an opening, and number of openings. Correlations between opening/closing actions and the conditions of the outdoor environment were analyzed. Findings indicate that occupants interact more with common room windows and tend to leave bedroom windows open for longer periods. Time of the day, temperature, and relative humidity influence both opening and closing actions. Solar irradiance is also an important driver for open actions, while mean wind speed is significant in predicting closing actions. Data-driven models were developed using logistic regression. Comparison between predicted and real data demonstrated a good performance in estimating the opening ratio over the year and seasonal variations but a tendency to overestimate the number of openings and underestimate the duration of an opening.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 teacher head, 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".