A Generalized Inhomogeneous Markov Chain Occupancy Model For Open-Plan Offices Using Real Time Locating System Data
Why this work is in the frame
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Bibliographic record
Abstract
A good occupancy prediction model requires enough input data pertinent to the occupants’ space utilization patterns. However, most of the occupancy detection systems cannot provide this detailed information, which reduces their practicality for open-plan offices. Therefore, there is a need to use proper sensing techniques to distinguish between different occupants in open-plan offices when detecting occupancy patterns. In this study, the probabilistic occupancy modelling has been further enhanced using inhomogeneous Markov chain prediction model based on data collected by a Real Time Locating System (RTLS). The comparison between the occupancy profiles resulting from the prediction model and the actual profiles showed that the prediction model was able to capture the behaviour of occupants. An adaptive probabilistic occupancy prediction model, which distinguishes the temporal behaviour of different occupants within an open-plan office, allows for the application of occupancy-centred local control strategies.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| 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 it