Progress and challenges in forecast verification
Bibliographic record
Abstract
ABSTRACT Verification scientists and practitioners came together at the 5 th I nternational V erification M ethods W orkshop in M elbourne, A ustralia, in D ecember 2011 to discuss methods for evaluating forecasts within a wide variety of applications. Progress has been made in many areas including improved verification reporting, wider use of diagnostic verification, development of new scores and techniques for difficult problems, and evaluation of forecasts for applications using meteorological information. There are many interesting challenges, particularly the improvement of methods to verify high resolution ensemble forecasts, seamless predictions spanning multiple spatial and temporal scales, and multivariate forecasts. Greater efforts are needed to make best use of new observations, forge greater links between data assimilation and verification, and develop better and more intuitive forecast verification products for end‐users. Copyright © 2013 Royal Meteorological Society
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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 | 0.125 | 0.188 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.002 | 0.009 |
| Scholarly communication | 0.012 | 0.024 |
| Open science | 0.008 | 0.008 |
| Research integrity | 0.006 | 0.009 |
| Insufficient payload (model declined to judge) | 0.009 | 0.004 |
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".