Some key challenges for subseasonal to decadal prediction research
Bibliographic record
Abstract
The practice of initialized subseasonal, seasonal and decadal climate prediction has matured considerably in recent years, with real-time subseasonal and decadal multi-system ensembles joining those established previously for the seasonal to multi-seasonal range. However, substantial scientific, modelling, and informational challenges remain that must be overcome in order to more fully realize the potential for such predictions to serve societal needs. This presentation will examine five such challenges that the World Climate Research Programme’s Working Group on Subseasonal to Interdecadal Prediction (WGSIP) has identified as crucial for further advancing capabilities for translating the inherent predictability of the Earth system into actionable predictive information. Surmounting these challenges will bring nearer an envisaged future in which global users have access to such information specific to individual needs, across Earth system components and on a continuum of time scales, with degrees of confidence, limitations and uncertainties clearly indicated, as well as tools to guide optimal actions.
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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 | 0.025 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.008 | 0.014 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.003 | 0.008 |
| Insufficient payload (model declined to judge) | 0.008 | 0.003 |
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".