Implementing Bayesian inference on a stochastic CO2-based grey-box model
Bibliographic record
Abstract
The COVID-19 pandemic brought global attention to indoor air quality (IAQ), which increases public’s awareness on monitoring indoor ventilation conditions significantly. Indoor CO 2 monitoring has been widely accepted as an effective way for indicating IAQ conditions, attributed to its close relationships with indoor air change rates. However, real-time estimation of air change rates or CO 2 emission rates from CO 2 measurement data remains challenging due to uncertainties in factors like random air movements, dynamic conditions (e.g., weather and occupancy), and the limitations of deterministic equations. This study addresses these challenges by applying Bayesian inference to a stochastic CO 2 -based grey-box model, enabling the accurate estimation of ventilation and CO 2 emission rates while accounting for uncertainty. The model’s accuracy and robustness were validated through CO 2 tracer gas experiments, employing constant injection and decay methods in a large-scale aerosol chamber. Both prior and posterior predictive checks (PPC) were performed to verify this approach. The approach proposed by this study improves the interpretation of CO 2 monitoring data, thereby facilitating the future real-time IAQ management.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".