An Information-Theoretical Score of Dichotomous Precipitation Forecast
Bibliographic record
Abstract
Abstract This study presents an information-theoretical score (ITS) with an emphasis on desirable and undesirable mutual information between a series of dichotomous forecast and observation. As ITS makes use of the same contingency table as traditional scores, the performance of threat score (TS), equitable threat score (ETS), and true skill statistics (TSS) are compared with ITS using three different approaches. First, a hypothetical forecast setup is employed to investigate the responses of the scores to bias, phase error, and event frequency. It was found that the desirable mutual information portion of ITS (C+) is closer to TSS, and the undesirable mutual information portion of ITS (C−) reveals the presence of biases and random errors in the forecast. There is also a similarity between ITS and ETS. Second, the sensitivities of ITS and ETS to forecast bias tendency are examined analytically using the critical performance ratio (CPR). It is shown that ITS has a more dynamical response to incremental bias. By increasing the bias, the CPR value of ITS increases more rapidly than that of ETS indicating a higher resistance to hedging. Third, the skill scores on two sets of operational forecasts are applied with respect to a mosaic of observed radar reflectivity. The results show that ITS remains more consistent in its evaluation of skills at different thresholds compared to other scores.
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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 | 0.007 | 0.048 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".