The Expansion and Modernization of Space Weather Ionospheric Network Canada
Bibliographic record
Abstract
Space Weather Ionospheric Network Canada (SWINCan), formerly the Canadian High Arctic Ionospheric Network (CHAIN), has provided continuous, near-real-time monitoring of the high-latitude ionosphere since 2007. Capitalizing on Canada’s geographic expanse and proximity to the northern magnetic pole, SWINCan’s expansive instrument network delivers high-latitude ionospheric data including essential space environment quantities for scientific and operational use. This data enables fundamental understanding of the ionosphere and its role in radio propagation and solar-terrestrial interactions, while also providing critical input for ionosphere nowcast/forecast models that support scientific research and operations of navigation, communication, and radar systems at sub-auroral, auroral, and polar latitudes. In response to growing demand for enhanced high-latitude observational capacity, the Radio and Space Physics Laboratory (RSPL) at the University of New Brunswick is in the process of substantially expanding and modernizing SWINCan. By 2026, this pan-Canadian network will consist of 128 global navigation satellite system (GNSS) ionospheric scintillation and total electron content monitors (GISTMs) and 20 modernized high-frequency (HF) ionospheric sounders, adding to the 28 GISTMs and 10 HF sounders that are currently deployed. SWINCan GISTMs record raw 50 Hz/100 Hz data enabling study of the multi-spatiotemporal-scale structuring of the ionosphere, including fundamental study of radio wave scintillation in a turbulent ionosphere. As part of SWINCan modernization, RSPL has also developed a state-of-the-art, versatile HF platform to enhance SWINCan ionosonde systems. Updated systems are specifically designed for harsh environments such as the Arctic, are fully and remotely configurable, and are capable of interdependent experiments with other ground and spaceborne radio systems.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 0.002 |
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".