Verif: A Weather-Prediction Verification Tool for Effective Product Development
Bibliographic record
Abstract
Abstract Verif is an open-source tool for verifying weather predictions against a ground truth. It is suitable for a range of applications and designed for iterative product development involving fine-tuning of algorithms, comparing methods, and addressing scientific issues with the product. The tool generates verification plots based on user-supplied input files containing predictions and observations for multiple point-locations, forecast lead times, and forecast initialization times. It supports over 90 verification metrics and diagrams and can evaluate deterministic and probabilistic predictions. An extensive set of command-line flags control how the input data are aggregated, filtered, stratified, and visualized. The broad range of metrics and data manipulation options allows the user to gain insight from both summary scores and detailed time series of individual weather events. Verif is suitable for many applications, including assessing numerical weather prediction models, climate models, reanalyses, machine learning models, and even the fidelity of emerging observational sources. The tool has matured through long-term development at the Norwegian Meteorological Institute and the University of British Columbia. Verif comes with an extensive wiki page and example input files covering a wide range of prediction applications, allowing students and researchers interested in verification to get hands-on experience with real-life datasets. This article describes the functionality of Verif version 1.3 and shows how the tool can be used for effective product development.
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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 | 0.013 | 0.061 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.007 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.082 | 0.033 |
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".