Calibration of hydrological model GR2M using Bayesian uncertainty analysis
Bibliographic record
Abstract
An outstanding issue of hydrological modeling is the adequate treatment of uncertainties in model calibration and prediction. The current paradigm is that the major sources of uncertainties, namely input, output and model uncertainty should be accounted for directly, instead of assuming they can be safely lumped into the output uncertainties. In this paper, Bayesian analysis is used to calibrate the conceptual hydrologic monthly model GR2M taking into account input, output, structural and initial state uncertainty through error models and priors. Calibration is performed under different error assumptions to study the influence of the initial state uncertainty, the consequences of large input errors, the impact of error assumptions on calibrated parameter posterior distributions and the definition of error models. It is shown how such an analysis can be used to separate, a posteriori , the different sources of errors, and in particular, to identify structural errors from data errors.
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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 | 0.001 | 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 | 0.001 | 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) | 0.001 | 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".