Applications of data assimilation to forecasting indoor environment
Bibliographic record
Abstract
Data assimilation (DA) is a technique to combine numerical predictions and experimental measurements, which has been commonly used by the community of numerical weather forecasting but is relatively new to the field of indoor environment. Among all data assimilation methods, Ensemble Kalman Filter (EnKF) is especially suitable to solve large-scale nonlinear problems due to its low computation requirement. In this paper, two new applications of EnKF to forecasting indoor environment are presented. The first study is to forecast the contaminant transport in a residential house based on a multi-room tracer gas decay experiment. The forecast model avoids calculating source term in the given problem by rapid updating model states with EnKF while still providing significant forecast lead time. The second study is to predict the fire smoke dispersion in a multi-room compartment fire without giving actual heat release rates (HRRs) as model inputs. Compared to conventional deterministic models, the EnKF models improve the predictions significantly.
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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) | 0.001 | 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".