Cell-DEVS Models for CO<sub>2</sub> Sensors Locations in Closed Spaces
Bibliographic record
Abstract
With the global warming crisis and its correlation to levels of energy consumption, it is paramount to find ways to reduce energy consumption in closed spaces with minimal disruption to occupants' comfort. Thus, researchers are working to improve methodologies for occupant-based demand-control heating, ventilation, and air conditioning. Sensor usage for occupancy detection is among the methodologies researched for controlling consumption. Carbon dioxide sensors proved to be effective but overly sensitive to configuration. Research also proved that there is an undetermined latency period between the changes of the number of occupants and the carbon dioxide sensors detection of that change. We present a work in progress method to determine the best placement of carbon dioxide sensors for the accurate occupants' detection and calculation of latency using the Cellular Discrete-Event Specifications formalism. We present several case studies showing resemblance between physical closed spaces and the models and how the simulation replicates real-life scenarios.
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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".