Large‐Scale Comparison of Polar Cap Ionospheric Velocities Measured by RISR‐C, RISR‐N, and SuperDARN
Bibliographic record
Abstract
Abstract The combined fields of view of the two Resolute Bay Incoherent Scatter Radars (RISR‐Canada and RISR‐North) significantly overlap the field of view of the Super Dual Auroral Radar Network (SuperDARN) radar located in Rankin Inlet. These radars measure ionospheric flow velocities in the polar cap region. Velocity data from the first multiple‐day combined operations of the two RISR radars and Rankin Inlet have been compared. Direct comparisons between line‐of‐sight measurements by both types of radars have been performed. These comparisons included data from 40 days of radar operations and used velocity data from 35 SuperDARN range gates (spanning 1,575 km). Overall, 5.2 × 105 comparison sets were analyzed. In particular during the daytime, signatures of groundscatter in the SuperDARN data often existed at most of the ranges considered in this comparison. This groundscatter could be partially removed from the comparison by only considering SuperDARN data points that had ionospheric velocity measurements in surrounding range cells. It was found that after removing groundscatter contamination at medium SuperDARN ranges (range gates 18–45), velocities measured by the two radar systems agreed when the high‐frequency results were adjusted to account for the refractive index effect. In regions dominated by groundscatter and E region scatter, lower SuperDARN velocities were measured and the overall comparison with the F region RISR velocities was poor.
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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 | 0.001 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".