Development of a Sparse Polynomial Chaos Expansions Method for Parameter Uncertainty Analysis
Bibliographic record
Abstract
Abstract Incorporating uncertainty assessment into hydrological simulation is of vital significance for providing valuable information for conserving and restoring the ecology environment in arid and semiarid regions. In this study, a sparse polynomial chaos expansions method was developed to quantify hydrological model parameter uncertainties on model performance in Kaidu river basin, China. A four dimension two order polynomial chaos expansions model was built and the effect of four parameters were quantified based on the coefficients of the polynomial chaos expansions model. Results indicated that precipitation in summer has more significant influence on model output than that in other seasons. High Sobol sensitivity indices values (0.22 in spring, 0.17 in summer, 0.21 in autumn and 0.29) for the interaction of precipitation and maximum capacity for fast store demonstrate that they are the major factors affecting runoff generation. These results can help reveal the flow processes and provide valuable information for water resources management.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".