New Developments of the Intensity-Scale Technique within the Spatial Verification Methods Intercomparison Project
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Bibliographic record
Abstract
Abstract The intensity-scale verification technique introduced in 2004 by Casati, Ross, and Stephenson is revisited and improved. Recalibration is no longer performed, and the intensity-scale skill score for biased forecasts is evaluated. Energy and its percentages are introduced in order to assess the bias on different scales and to characterize the overall scale structure of the precipitation fields. Aggregation of the intensity-scale statistics for multiple cases is performed, and confidence intervals are provided by bootstrapping. Four different approaches for addressing the dyadic domain constraints are illustrated and critically compared. The intensity-scale verification is applied to the case studies of the Intercomparison of Spatial Forecast Verification Methods Project. The geometric and synthetically perturbed cases show that the intensity-scale verification statistics are sensitive to displacement and bias errors. The intensity-scale skill score assesses the skill for different precipitation intensities and on different spatial scales, separately. The spatial scales of the error are attributed to both the size of the features and their displacement. The energy percentages allow one to objectively analyze the scale structure of the fields and to understand the intensity-scale relationship. Aggregated statistics for the Storm Prediction Center/National Severe Storms Laboratory (SPC/NSSL) 2005 Spring Program case studies show no significant differences among the models’ skill; however, the 4-km simulations of the NCEP version of the Weather Research and Forecast model (WRF4 NCEP) overforecast to a greater extent than the 2- and 4-km simulations of the NCAR version of the WRF (WRF2 and WRF4 NCAR). For the aggregated multiple cases, the different approaches addressing the dyadic domain constraints lead to similar results. On the other hand, for a single case, tiling provides the most robust and reliable approach, since it smoothes the effects of the discrete wavelet support and does not alter the original precipitation fields.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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