Inter-Comparison of Different Bayesian Model Averaging Modifications in Streamflow Simulation
Bibliographic record
Abstract
Bayesian model averaging (BMA) is a popular method using the advantages of forecast ensemble to enhance the reliability and accuracy of predictions. The inherent assumptions of the classical BMA has led to different variants. However, there is not a comprehensive examination of how these solutions improve the original BMA in the context of streamflow simulation. In this study, a scenario-based analysis was conducted for assessment of various modifications and how they affect BMA results. The evaluated modifications included using various streamflow ensembles, data transformation procedures, distribution types, standard deviation forms, and optimization methods. We applied the proposed analysis in two data-poor watersheds located in northern Ontario, Canada. The results indicate that using more representative distribution types do not significantly improve BMA-derived results, while the positive effect of implementing non-constant variance on BMA probabilistic performance cannot be ignored. Also, higher reliability was obtained by applying a data transformation procedure; however, it can reduce the results’ sharpness significantly. Moreover, although considering many streamflow simulations as ensemble members does not always enhance BMA results, using different forcing precipitation scenarios besides multi-models led to better BMA-based probabilistic simulations in data-poor watersheds. Also, the reliability of the expectation-maximization algorithm in estimating BMA parameters was confirmed.
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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".