Spherical cap harmonic analysis of Super Dual Auroral Radar Network (SuperDARN) observations for generating maps of ionospheric convection
Bibliographic record
Abstract
A spherical cap harmonic analysis (SCHA) technique is introduced for mapping the 2‐D high‐latitude ionospheric convection pattern based on Super Dual Auroral Radar Network (SuperDARN) velocity measurements. The current method for generating such maps is the FIT technique which generates global‐scale maps over the entire convection region. This is accomplished by combining observations with a statistical model to prevent unphysical solutions in areas away from the observation points and by forcing the plasma flow to zero at the low‐latitude boundary of the convection zone. Both constraints distort the mapped convection and require a preconception of where the plasma flow lines should close. By focusing on mapping the convection over a region well covered by velocity observations, the SCHA technique is freed of these constraints and more accurately reproduces local convection. We generate large‐scale convection maps from SuperDARN data for various interplanetary magnetic field (IMF) conditions during periods of widespread radar coverage to show the patterns are consistent with expectations for various IMF configurations. We validate the SCHA maps by comparing them with the 2‐D ion drifts measured by the DMSP satellites and with the 2‐D convection vectors obtained by merging SuperDARN measurements at beam crossings. The SCHA technique is shown to perform comparably to the FIT technique over regions of good data coverage. For limited data coverage and over regions of highly variable flow, particularly near the equatorward edge of the mapping region, the SCHA technique provides a better solution for mapping ionospheric convection based on SuperDARN radar observations.
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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 | 0.001 |
| 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) | 0.002 | 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".