Space Weather Ionospheric Network Canada (SWINCan)
Bibliographic record
Abstract
Space Weather Ionospheric Network Canada (SWINCan) will establish a pan-Canadian infrastructure of ground-based sensors that will provide state-of-the-art, real-time monitoring of the ionosphere spanning polar, auroral, and sub-auroral latitudes. SWINCan is an expansion and modernization of the Canadian High Arctic Ionospheric Network (CHAIN), one of the world’s largest networks for ionospheric research operated by the Radio and Space Physics Laboratory (RSPL) at the University of New Brunswick (UNB). SWINCan will deploy 100 specialized Global Navigation Satellite System (GNSS) receivers and 10 modular ionospheric sounder (MODIS) systems across Canada, while enhancing the 28 GNSS and 10 ionosonde systems currently installed in the Canadian Arctic as part of CHAIN. SWINCan GNSS receivers are high-rate (100 Hz) ionospheric scintillation and total electron content (TEC) monitors (GISTMs) that will provide real-time data and enhance multi-scale observation of the ionospheric structure and dynamics. MODIS systems being developed by RSPL are next generation, low power high frequency (HF) systems that take advantage of the latest developments in software defined radio and signal processing technology to reduce power consumption and increase ionospheric measurement capabilities in harsh, remote environments such as the Canadian Arctic. SWINCan is designed to take advantage of the unique natural laboratory of the Canadian Arctic for the fundamental study of solar-terrestrial interactions, and will provide essential input for mitigation of space weather effects on modern technological systems such as GNSS, radio communication, and over-the-horizon-radar, services critical to social, military, science, and major economic sectors.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.029 | 0.011 |
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".