D2.3. Report on potential science - industry priorities in research and observations
Notice bibliographique
Résumé
In the context of the global climate change, where the Arctic sea ice has been shrinking with acceler-ating losses in the last two decades starting to make commercially viable sea routes through the Arctic, the ARICE project aims at establishing an international cooperation strategy to better coordi-nate the existing polar research fleet, to offer transnational access to a set of international High Arctic research icebreakers, and to collaborate with maritime industry in a “programme of ships and plat-forms of opportunity”. The achievement of these goals represents a fundamental step to provide information on the state of the Arctic Ocean that is urgently needed due to the fast increase of the Arctic marine traffic. Safe navigation and voyage planning in Arctic waters as well as sustainability in operations, in particular concerning environmental aspects related to shipwrecks, oil spill risk, ship-ping impacts, underwater noise, invasive species, require improved weather and sea ice forecast, that has to be supported by investments in hydrographic, meteorological and oceanographic data. In particular, safe navigation requires additional hydrographic surveys to improve Arctic navigation charts, and systems to support realtime acquisition, analysis and transfer of meteorological, ocean-ographic, sea ice and iceberg information. Results in this direction can be achieved only through international cooperation not limited to the scientific community but extended to industry involved in Arctic exploitation and services or in some way impacted by Arctic climate changes. With the awareness that “science-stakeholder connection follows an iterative process: iteration ensures better adjustment of the research priorities to the so-ciety expectations”1, the ARICE project, starting from previous activities carried out by the Interna-tional Arctic Science Committee (IASC) and EU-PolarNet project, promoted a path of interactions be-tween the scientific community and industrial stakeholders in the Arctic which allowed to identify in a first phase common themes of research, innovation and technological development, and specific industrial research interests in a second phase. Furthermore, the discussion highlighted the absence of instruments capable of carrying out automatic measurements in the field of physic-chemical quan-tities of significant scientific interest, opening the way for the development of new products by high-tech companies. This activity was mainly supported by the organization of a research-industry session at Arctic Circle Assembly 2019 in cooperation with EU polar cluster members and a side event Workshop at the Sustainable Ocean Summit 2019. This report is organized as it follows. Previous activities aiming at connecting science and industrial stakeholders are summarized and discussed in section 2. Section 3 reports ARICE workshop activities and contributions with an overall discussion of their results identifying potential science-industry pri-orities in research and observation. Major cooperation, industrial and science needs are reported. Concluding remarks will summarise open issues and possible future steps.
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 | 0,009 | 0,009 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,001 |
| Bibliométrie | 0,005 | 0,003 |
| Études des sciences et des technologies | 0,002 | 0,000 |
| Communication savante | 0,006 | 0,002 |
| Science ouverte | 0,002 | 0,003 |
| Intégrité de la recherche | 0,005 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,051 | 0,043 |
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 ».