Stochastic Calibration of Riverine Water Quality Models
Bibliographic record
Abstract
Though commonly used, the suitability of deterministic calibration criteria for stochastic model calibration and uncertainty analysis is unclear. The purpose of this paper is to examine the suitability, relative benefits, and substantial disadvantages of "deterministic-optimization" approaches, such as root mean square error (RMSE), in stochastic contexts. Three alternate calibration strategies that are suitable for stochastic modeling of water quality under uncertainty are proposed and then demonstrated. The three alternate strategies are the absolute relative error (ARE), weighted relative error, and stochastic exceedance calibration strategy. The findings suggest that potential improvements can be made to current calibration paradigms. The alternate calibration strategies, all of which are based on relative error, were found to match or exceed RMSE calibration strategies, in terms of overall performance, with the enhancement of superior model surface-response characteristics. Additionally, the application of more stringent ARE criteria offered greater improvement in the stochastic calibration response than increasing the RMSE threshold criteria. Several qualitative benefits of ARE and related metrics also were shown. Because many environmental systems and almost all water quality models are subject to substantial uncertainty, approaches such as those proposed hold substantial, widely applicable benefits.
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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 | 0.002 | 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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; both teacher heads agree on what is shown here.
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".