Prototyping cutting edge science: the EUCP project experience
Notice bibliographique
Résumé
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.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,041 | 0,035 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,001 | 0,002 |
| Études des sciences et des technologies | 0,004 | 0,010 |
| Communication savante | 0,010 | 0,009 |
| Science ouverte | 0,005 | 0,018 |
| Intégrité de la recherche | 0,005 | 0,005 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,009 | 0,003 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».