Bibliographic record
Abstract
As industry pushes towards more sustainable design, an essential tool that helps designers quantify energy usage of a building is building energy simulation. However, researchers have noticed that there is a gap between the estimated and measured energy use. One aspect contributing to this discrepancy is inaccurate data-driven occupancy behaviour models. To create better models, researchers need to obtain data in a new method that does not produce inauthentic data. Ex-situ camera-based occupant behaviour monitoring has been proposed as a solution. This study uses image processing technologies to identify the windows on a façade and determine their individual state (i.e. open, partially open, or closed). The algorithm developed in this study yields a 90% accuracy rate over all the windows tested. This algorithm is specifically targeted at punched façades with awning windows. Factors that affected the accuracy of ex-situ camera-based occupant behaviour monitoring include environmental conditions such as lighting, obstructions, and reflections. Furthermore, there are challenges in thresholding and identifying the significant peaks for window angle image data. The next steps of this research should determine appropriate threshold values through additional testing and explore new image-processing techniques for other window types.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".