Application Potential of Four Nontraditional Similarity Metrics in Hydrometeorology
Bibliographic record
Abstract
Abstract This paper presents a review and assessment of four nontraditional similarity metrics that can be applied to hydrological and meteorological data. These metrics are 1) the uncentered correlation coefficient, 2) the Hodgkin–Richards index, 3) the Petke index, and 4) the Wang–Bovik index. The first metric has been widely used in hydrometeorology, and the other three have been proposed in other disciplines for similarity analysis. It is demonstrated that these similarity metrics, in their original formulations, either do not actually have the purported advantage over the traditional Pearson correlation coefficient or are not suitable for some hydrometeorological applications. They are reformulated in this study to address these deficiencies. The resulting modified metrics are unitless, bounded, and proportional to the Pearson correlation coefficient, and three of them have the confirmed advantage of explicitly penalizing for differences in the mean and/or in the variance. Two application examples are used to demonstrate the applicability of these similarity metrics in hydrometeorology. A metavalidation model and a graphical tool (Taylor diagram) are used to evaluate the performances of these similarity metrics. In a case study of analog analysis, the Wang–Bovik index stands out as the best metric for simulation of the human perception of similarity between two-dimensional patterns, whereas the modified Petke index and the traditional root-mean-square distance may perform slightly better than the others in the regions with a very large difference between the variances.
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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 | 0.006 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.009 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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 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".