A digital Circum-Arctic geological repository from the NORRAM project
Notice bibliographique
Résumé
Most of the Arctic region is contained within the territory of Norway, Russia, USA, Canada and Denmark/Greenland, yet the natural boundaries and processes do not conform to these political borders. This remote region requires special logistics, equipment and substantial financial support. The last decade has seen an increase in knowledge about the northern polar region for economic and political reasons, such as the extended continental shelf claims under UNCLOS and Arctic Council activities. It is crucial that scientific research, activities and their outcome are visible to the broader scientific community and communicated to the wider public. In recent years considerable effort has been invested by several groups and institutions to make various data and results available online and to use it for education and outreach. Examples include: the Arctic Observing Viewer which is a web mapping application in support of U.S. SEARCH, AON, SIOS, and other Arctic Observing networks (https://arcticobservingviewer.org/); Arctic Research Mapping Application (https://armap.org/) and the NSF Arctic Data Center (https://arctic data.io) for locating projects and data supported by US funding agencies; Svalbox (www.svalbox.no), a database for digital outcrop models from Svalbard, the comprehensive PANGAEA database (https://www.pangaea.de), a data publisher for Earth and Environmental sciences; and GeoMapApp (http://www.geomapapp.org/), a map-based application for browsing, visualizing and analyzing a diverse suite of curated global and regional geoscience data sets. While a wealth of data can be located and viewed in these databases and data repositories, the scientific community and geoscience educators may benefit from a collection of geological and geophysical data that can be easily visualized, analyzed and used for a quick assessment of present-day geodynamic setting and further for paleogeographic reconstructions in the circum-Arctic region. Consequently, a group of scientists from four Arctic countries and their collaborators are aiming to consolidate and further develop the Arctic-related common scientific basis and educational programmes under the auspices of the Norwegian Research Council programme INTPART (International Partnerships for Excellent Education, Research and Innovation). The project NOR-R-AM (https://norramarctic.wordpress.com/), established in 2017, focused on assessing the openly available information accumulated at participating institutes. During the first phase of this project, we have gathered and interpreted data in various sub-regions, especially in Svalbard and in Russia. The second phase of the NOR-R-AM project aims to complete and launch the digital Circum-Arctic geodynamics platform. This web-based platform will incorporate geological and geophysical data and models, tomographic and kinematic models and paleogeography and paleoclimate indicators. The digital Circum-Arctic geological repository, to be hosted by our project webpage https://norramarctic.wordpress.com/, assembles the data in openly accessible formats that are compatible with GPlates, GeomapApp and Google Earth. These data are consistently formatted to simplify exchange and completely open to the scientific community.
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 | 0,002 | 0,005 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,008 | 0,016 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,004 | 0,003 |
| Science ouverte | 0,002 | 0,003 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,206 | 0,187 |
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 ».