Canadian High Arctic Ionospheric Network (CHAIN)
Bibliographic record
Abstract
Polar cap ionospheric measurements are important for the complete understanding of the various processes in the solar wind‐magnetosphere‐ionosphere system as well as for space weather applications. Currently, the polar cap region is lacking high temporal and spatial resolution ionospheric measurements because of the orbit limitations of space‐based measurements and the sparse network providing ground‐based measurements. Canada has a unique advantage in remedying this shortcoming because it has the most accessible landmass in the high Arctic regions, and the Canadian High Arctic Ionospheric Network (CHAIN) is designed to take advantage of Canadian geographic vantage points for a better understanding of the Sun‐Earth system. CHAIN is a distributed array of ground‐based radio instruments in the Canadian high Arctic. The instrument components of CHAIN are 10 high data rate Global Positioning System ionospheric scintillation and total electron content monitors and six Canadian Advanced Digital Ionosondes. Most of these instruments have been sited within the polar cap region except for two GPS reference stations at lower latitudes. This paper briefly overviews the scientific capabilities, instrument components, and deployment status of CHAIN. This paper also reports a GPS signal scintillation episode associated with a magnetospheric impulse event. More details of the CHAIN project and data can be found at http://chain.physics.unb.ca/chain.
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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 | 0.002 | 0.004 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.025 | 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".