Sensitivity and Uncertainty Analysis of a Fire Spread Model with Correlated Inputs
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Bibliographic record
Abstract
Sensitivity and uncertainty analysis is a very important tool to identify and treat model uncertainties in quantitative fire risk analysis. An existing Fire Spread model with correlated input variables are presented for sampling-based sensitivity analysis, and selected input variables include fire growth rate, fire resistance rating and its standard deviation, fire load density and its standard deviation. A sampling approach is proposed to deal with the correlated structure of input variables, which introduces a noise term and can transform correlated input variable structure into an independent one. Furthermore, sensitivity analysis of input variables of fire spread model is performed and an order of variable sensitivity is given. Results show that fire resistance rating and its standard deviation are two very important input variables while standard deviation of fire load density is the least sensitive parameter. Further discussions are provided on the effectiveness of the sampling technique and the use the results of the analysis.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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 it