A digital Circum-Arctic geological repository from the NORRAM project
Bibliographic record
Abstract
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.008 | 0.016 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.206 | 0.187 |
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".