Bibliographic record
Abstract
decision tree, building performance The ability to accurately determine localized building occupancy in real time enables several compelling applications, including intelligent control of building systems to minimize energy use and real-time building visualization. Having equipped an office workspace with a heterogeneous sensor array, our goal was to use the sensors in tandem to produce a real-time occupancy detector. We used Decision Trees to perform the classification and to explore the relationship between different types of sensors, features derived from sensor data, and occupancy. We found that the individual feature which best distinguished presence from absence was the root mean square error of a passive infrared motion sensor, calculated over a two-minute period. When used with a simple threshold, this individual feature detected occupancy with 97.9 % accuracy. Combining multiple motion sensor features with a decision tree, the accuracy improved to 98.4%. Counterintuitively, the addition of other types of sensors, such as sound, CO2, and power use, worsened the classification results. The implication is that, while Decision Trees may improve occupancy detection systems based on motion sensors alone, one risks overfitting if multiple types of sensors are combined. 1
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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.088 | 0.020 |
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".