Bibliographic record
Abstract
Recent years have witnessed a steady increase in the number of occupancy detection algorithms and people counting systems designed for residential and commercial buildings, yet comparing the accuracy of existing solutions has been impossible to date due to the lack of publicly available test data sets, open-source implementation of the state-of-the-art algorithms, and consensus on the evaluation metrics. This paper addresses this problem by presenting the design and implementation of an open-source toolkit for occupancy detection. ODToolkit is capable of importing and converting sensor data acquired from various buildings into a common data format, provides implementation of a broad suite of data-driven occupancy detection techniques, and calculates a set of evaluation metrics for each experiment. We present several case studies to show how this toolkit facilitates the development of new occupancy detection algorithms. In particular, we extend this toolkit by implementing novel domain-adaptive occupancy detection algorithms and compare them with the benchmark supervised learning algorithms on multiple data sets. Furthermore, we investigate what sensing modalities and precision are needed to achieve a desired level of accuracy for occupancy estimation through sensor fusion. ODToolkit code and documentation are available at https://odtoolkit.github.io/.
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 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.012 |
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".