Model Variable Augmentation (MVA) for Diagnostic Assessment of Sensitivity Analysis Results
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract Sensitivity analysis (SA) is a critical part in the construction of all models, including environmental and water resources simulation models. For example, SA functions to characterize which model inputs the model outputs are overly sensitive or insensitive to. However, the quality of SA results is rarely assessed. If assessed, bootstrapping of the sensitivity results is used to determine the reliability of the SA output. Bootstrapping, however, is known to be inappropriate with small sample sizes. In contrast, increasing model computational burdens continues to drive researchers to apply existing SA techniques and develop new ones, with smaller and smaller sample sizes. The new Model Variable Augmentation (MVA) approach is therefore introduced here to assess the quality of SA results without performing any additional model runs or requiring bootstrapping. MVA augments the original model input variables with additional variables of known properties. The sensitivities of the augmented model variables are used to draw conclusions on the reliability of the other “original” model parameters' sensitivities. The MVA method is applied to two global SA methods: the variance‐based Sobol' method and the moment‐independent PAWN method. MVA is scrutinized using analytical benchmark functions and then used to quality check sensitivity results of two hydrologic models. Results show the following: (1) MVA is a framework to quality check the implementation of a SA method; (2) for Sobol' and PAWN analyses, MVA‐assisted ranking of input sensitivity measures outperforms the standard ranking procedure without MVA; and (3) MVA provides reasonable estimation of the uncertainty of sensitivity estimates.
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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 | 0.019 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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 it