Better late than never: arrival of decadal predictions to the climate services arena
Bibliographic record
Abstract
Decadal prediction represents a source of near-term climate information that has the potential to support climate-related decisions in key socio-economic sectors that are influenced by climate variability and change. While the research to illustrate the ability of decadal predictions in forecasting the varying climate conditions on a multi-annual timescale is rapidly evolving, the development of climate services based on such forecasts is still in its early stages. This study showcases the potential value of decadal predictions in the development of climate services. We summarize the lessons learnt from coproducing a forecast product that provides tailored and user-friendly information about multi-year drought conditions for the coming five years over global wheat harvesting regions. The interaction between the user and climate service provider that was established at an early stage and lasted throughout the forecast product development process proved fundamental to provide useful and ultimately actionable information to the stakeholders concerned with food production and security. This study also provides insights on the potential reasons behind the delayed entry of decadal predictions in the climate services discourse and practice, which were obtained from surveying climate scientists and discussing with decadal prediction experts.
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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 | 0.032 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.008 | 0.010 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.009 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".