Polarization and m $m$‐Number Characteristics of Mid‐Latitude Pc5 ULF Waves Observed by SuperDARN Radars
Bibliographic record
Abstract
Abstract Polarization and propagation characteristics of ultra‐low frequency (ULF, mHz) waves are conventionally studied using arrays of ground‐based magnetometers. However, the ground magnetometer observations are subject to distortions due to polarization rotation and spatial integration effects caused by the transition of the magnetohydrodynamic wave into an electromagnetic wave at the lower ionospheric boundary. In contrast, high‐frequency (3–30 MHz) radars, like those comprising the Super Dual Auroral Radar Network (SuperDARN), are capable of direct observations of the ULF wave characteristics at ionospheric altitudes via measuring plasma drift velocity variations caused by the wave's electric field. In this work, we use multi‐beam data from SuperDARN Hokkaido East, Hokkaido West, and Christmas Valley West radars to identify the dominant polarization modes as well as azimuthal wave numbers of evening‐night‐side‐morning ULF waves in the Pc5 frequency band (1.67–6.67 mHz) propagating over sub‐auroral and mid‐latitude regions. The observed statistical characteristics of these waves point at the solar wind dynamic pressure variations and Kelvin‐Helmholtz instability at the magnetopause as their potential principal sources, although the drift‐bounce resonance with trapped energetic ions may contribute to the small‐scale part of the observed Pc5 wave population.
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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 | 0.000 |
| 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) | 0.001 | 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".