Towards Conditional Parameter Estimation for Automatic Model Structure Identification: Using Mixed-Integer Calibration for Model Development
Bibliographic record
Abstract
A recently introduced framework for Automatic Model Structure Identification (AMSI) allows to simultaneously optimize model structure choices (integer decision variables) and parameter values (continuous decision variables) in hydrologic modelling. By combining the mixed-integer optimization algorithm DDS and the flexible hydrologic modelling framework RAVEN, AMSI is able to test a vast number of model structure and parameter combinations in order to identify the most suitable model structure for representing the rainfall runoff behavior of a catchment. The model structure and all potentially active model parameters are calibrated simultaneously. This causes a certain degree of inefficiency during the calibration process, as variables might be perturbed that are not currently relevant for the tested model structure. In order to avoid this, we propose an adaption of the current DDS algorithm allowing for conditional parameter estimation. Parameters will only be perturbed during the calibration process if they are relevant for the model structure that is currently tested. The conditional parameter estimation setup will be compared to the standard DDS algorithm for multiple AMSI test cases. We will show if and how conditional parameter estimation increases the efficiency of AMSI.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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; a candidate call from one source (direct Gemma or distilled Codex), 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".