Space physics in Svalbard: A study of the energy input in the polar ionosphere using SuperDARN
Bibliographic record
Abstract
This is chapter 2 of the State of Environmental Science in Svalbard (SESS) report 2020 (https://sios-svalbard.org/SESS_Issue3). The chapter provides an overview of Norwegian space physics infrastructure in Svalbard (owned either individually or through collaborations) with a particular focus on the Svalbard SuperDARN (Super Dual Auroral Radar Network) radar. This new radar, located on Breinosa near the Kjell Henriksen Auroral Observatory (KHO), is the only Norwegian-owned radar in a global network of more than 30 radars. They are designed for studying flows and turbulence in the upper atmosphere (100-300 km altitude), driven by interactions between the magnetic fields of the Sun and the Earth. The Svalbard SuperDARN radar fills an important gap in the spatial coverage of SuperDARN and complements the other research infrastructure mentioned in the report. The radar operated continuously from October 2016 – October 2018, before being damaged by a large ice storm. It will be rebuilt in 2021. The report highlights the important scientific achievements of the radar, with an emphasis on localised upper atmospheric processes and studies of a more global nature.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".