Arctic Tectonics and Volcanism: a multi-scale, multidisciplinary educational approach
Bibliographic record
Abstract
Abstract. Geologically, the Arctic is one of the least explored regions of Earth. Its significance, in terms of indigenous populations, resource extraction, tourism, shipping and a rapidly changing climate, is increasing. The Arctic offers geological diversity encompassing onshore and offshore environments, include active subduction zones in Alaska, deep sedimentary basins on the Siberian and Barents Sea shelves, widespread ancient Arctic volcanism and magmatism, the world’s slowest spreading mid-ocean ridge (Gakkel Ridge in the Eurasia Basin), as well as world-class examples of extensional and compressional basins exposed onshore Svalbard. Obtaining data is logistically, economically and environmentally expensive in the high Arctic, but the township of Longyearbyen at 78° N represents a relatively easily accessible gateway to Arctic geology. The year-round settlement on Spitsbergen, the main island of the Svalbard archipelago is home to The University Centre in Svalbard (UNIS). Reached by a year-round airport with regular connections to mainland Norway, Svalbard provides a foundation from which to teach and explore Arctic geology via the classroom, the laboratory, and the field. In this contribution, we present a new graduate course (Masters and PhD level) on Arctic Tectonics and Volcanism that we have established and taught annually at UNIS since 2018. We outline the course itself, before presenting student perspectives based on both an anonymous questionnaire (n = 27) and in-depth perceptions of four selected students. The course, with an intake of up to 20 MSc and PhD international students, is held over a 6-week period, typically in Spring or Autumn. The course comprises modules on field and polar safety, Svalbard/Barents Sea geology, wider Arctic geology, plate tectonics, mantle dynamics, geo- and thermochronology, and geochemistry of igneous systems. All modules include individual and group-based exercises in addition to introductory lectures. A field component, which in some years included an overnight expedition, provides an opportunity to appreciate Arctic geology and gather own field observations and data. Digital outcrop models and photospheres viewed with state-of-the-art visualization in the classroom facilitate efficient fieldwork through pre-fieldwork preparation and post-field work quantitative analyses. The course assessment is centered on an individual research project that is presented orally and in a short and impactful Geology journal-style article. Apart from the course at UNIS we have jointly initiated several one-off research and education-based events at partner institutions, and briefly outline these.
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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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.010 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.022 | 0.006 |
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".