Model Variable Augmentation (MVA) for Diagnostic Assessment of Sensitivity Analysis Results (v1.0)
Bibliographic record
Abstract
Model Variable Augmentation (MVA) for Diagnostic Assessment of Sensitivity Analysis Results by Juliane Mai and Bryan A Tolson (University of Waterloo, Canada) Version 1.0 (Jan 2020) Abstract The method of Model Variable Augmentation (MVA) was introduced to assess the quality of SA results without performing any additional model runs or requiring bootstrapping. MVA is proven to perform well when only a small number of model runs was used to obtain the sensitivity indexes. 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 already successfully tested with two global SA methods: the variance-based Sobol' method and the moment-independent PAWN method. The full paper can be found here. Step-by-Step Tutorial The step-by-step tutorial describes all the steps to estimate sensitivity indexes for (original) model variables and the augmented parameters. It also explains how to analyse these results and how to draw conclusions on the reliablility of the sensitivity indexes of the original model variables. Details can be found here. Examples We provide some case studies to show how MVA can help: to check the implementation of the sensitivity analysis method (see here) to obtain a robust ranking of the model variables (see here) to estimate the uncertainty of the sensitivity indexes without the necessity of bootstrapping (see here) Citation J Mai & BA Tolson (2019). Model Variable Augmentation (MVA) for diagnostic assessment of sensitivity analysis results. Water Resources Research, 55, 2631– 2651. https://doi.org/10.1029/2018WR023382
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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 | 0.038 | 0.129 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.002 | 0.005 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.002 | 0.006 |
| Insufficient payload (model declined to judge) | 0.084 | 0.019 |
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".