Prototyping cutting edge science: the EUCP project experience
Bibliographic record
Abstract
At national and European levels development of climate services is seen as a bridge between climate research and decision makers, meant to mitigate and create a sound basis to adapt to Climate Change. To enhance the quality and relevance of climate services, several actors, namely users, providers, purveyors, and researchers participate to identify and provide through co-design, co-development, and co-delivery the improvements and innovations in climate services that are needed to better inform decision-making processes. Strengthening the two-way interaction between climate modelers and climate service providers will enhance the scientific basis for these services and the relevance of climate research and modelling outputs.The H2020 EUCP (European Climate Prediction System) project aimed to produce climate information to deliver to intermediate users, such as climate service providers and consultants, that ultimately should enter the decision domain. For this reason, one of the main objectives of the project was to produce prototypes to showcase how project’s resulting climate information could be used in the real world and how they can make a difference. One of the main goals of the engagement approach in the EUCP project is to reduce the gap between ‘top-down’ climate information driven by science and ‘bottom-up’ end-user requirements to increase the credibility and usability of climate information. This is a major barrier to the use of climate information in decision making at present. To overcome this barrier, it is widely recognized that prototyping is a key element that allows users to understand the “science behind” as well as how it could be applied in specific case studies providing valuable comments to improve the prototypes to close the gap with end users.Similarly, to what happens in producing operational climate services, EUCP prototype production was based on a cycle of prototyping through user trials following the 5Es approach: Explore, Exploit, Expose, Examine, and Expand. It is not a series of sequential steps but an iterative process where a step forward does not imply leaving that stage and not considering it anymore. For instance, understanding the users’ needs is a step that should be considered many times during the development; at the beginning understanding users’ needs should inform the scientific community about research areas of interests, then, user needs should affect how results are shown through an effective display.This presentation reviews how the 5Es approach was developed throughout the project, who were the actors involved and what instruments for users’ engagement were applied and used in this framework. Moreover, some examples of prototypes will be discussed in detail demonstrating how the 5Es approach is flexible enough to prototype different products. Finally, some lessons learnt in the project will be summarized as guidelines for future research.
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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 | 0.035 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.004 | 0.010 |
| Scholarly communication | 0.010 | 0.009 |
| Open science | 0.005 | 0.018 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.009 | 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".