Deliverable 1.10 Roadmap for a sustainable Arctic Observing System
Notice bibliographique
Résumé
The INTAROS Roadmap describes the way forward to improve and sustain the observing capacity in the Arctic. The Roadmap addresses the full data delivery chain from observing sensors to data repositories with focus on in situ observations. The document describes key factors determining how well an observing system can function in the Arctic, involving technological advances, infrastructure and data networks. Furthermore, the document emphasize the importance of cross disciplinary collaboration and stakeholder engagement as part of the data delivery chain. The development of in situ observing systems in the Arctic, especially ocean-based observations in the ice-covered regions, depends heavily on mature technology, transport infrastructures and logistical services allowing personnel to access the areas. Deployment and operation of observing platforms require use of icebreakers, aircraft, manned ice stations and automated systems that can operate year-round Based on the experience and knowledge of the INTAROS consortium, the following recommendations are formulated: The importance of in situ observations must be promoted as the backbone for building knowledge about climate and environmental change in the Arctic, at the same level as satellite observations and modelling systems The funding mechanisms for in situ observing systems need to be strengthened and coordinated between programmes, projects and institutions involved in Arctic observation, including local communities The Joint Statement of Ministers (ASM 2021), signed by 25 countries and six Indigenous Peoples organizations, states that they agree to strengthen cooperation on implementing Arctic observing and data sharing, implying that they need to allocate resources for in situ measurements contributing to the observing systems. Technology development for more robust and reliable in situ observing systems is needed. Here, major industry actors can play a role by investing in platforms and sensors that can operate autonomously in the Arctic The data delivery chain from in situ observing systems must be operationalised for each of the discipline-oriented systems in order to facilitate data sharing. This requires collaboration between the research communities, data services and other actors involved in the delivery chain. Collaboration can be enhanced by setting up mediators who can communicate between the actors Observing systems must be adapted to evolving priorities, requirements, and technological developments. This requires regular dialogue with researchers, stakeholders in private and public sector, researchers, service providers, local communities and Indigenous rightholders in the Arctic. Competence building need to be strengthened in observing methods, technologies, and procedures across gender and generations. The INTAROS roadmap builds on the experience and knowledge from the INTAROS consortium comprising more than 300 scientists from 49 institutions in Europe, Asia, and North America. In addition the document builds on discussions with representatives of Indigenous and local communities, private and public stakeholders, scientists and service providers at more than 50 workshops organised by the INTAROS project.
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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 | 0,007 | 0,010 |
| Méta-épidémiologie (sens strict) | 0,002 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,003 | 0,002 |
| Études des sciences et des technologies | 0,002 | 0,001 |
| Communication savante | 0,006 | 0,006 |
| Science ouverte | 0,003 | 0,006 |
| Intégrité de la recherche | 0,004 | 0,003 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,229 | 0,207 |
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 ».