Building storylines for applications: what have we learned in the EUCP project?
Bibliographic record
Abstract
The European Climate Prediction system (EUCP) project aimed to lay the foundation for a future regional climate prediction system for Europe. An important element of this is the role of narrative or storylines approaches in data production and scientific investigation, as well as a user product. We will present the results of our investigations which sought to understand the potential advantages and challenges in developing physically based climate storylines as part of a climate service by addressing the questions What are climate storylines and where are they useful? How could storylines bring together various outputs of EUCP state-of-the-art climate science attempting to reduce uncertainty and complexity in climate projections, and seamlessly combine them with decadal predictions? What are the challenges of producing them as a service? The body of EUCP work included two case studies of co-producing storylines as a user product, revealing the potential usefulness for applications. These included the heritage and water supply management sectors which are at different stages of adaptation management. We also reflect on the potential of event-based future storylines: one using a convection-permitting model to provide attribution statements for the Copenhagen flooding event in July 2011 and another using large ensembles to construct storylines of the European summer 2018 drought under different pseudo-global warming. Novel scientific studies were also performed which could form the scientific building blocks of climate storylines including an algorithmic clustering approach and approaches to producing more realistic climate variability. Finally, we will present a tool for performing multiple lines of evidence assessments that could aid storylines 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 | 0.041 | 0.104 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.004 | 0.011 |
| Scholarly communication | 0.030 | 0.057 |
| Open science | 0.007 | 0.013 |
| Research integrity | 0.006 | 0.010 |
| Insufficient payload (model declined to judge) | 0.032 | 0.013 |
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".