Spatial structure of ULF waves: Comparison of magnetometer and Super Dual Auroral Radar Network data
Bibliographic record
Abstract
The spatial structure of ultralow frequency (ULF) waves is usually, though not exclusively, estimated from ground‐based magnetometer measurements. This paper compares ULF wave spatial structure obtained from coincident ground magnetometer and HF radar measurements and addresses the interpretation of Pc5 azimuthal wave numbers. ULF spatial structures estimated from magnetometer and radar data were quite different for the October 23, 1994, event presented by Ziesolleck et al. [1998]. Azimuthal wave numbers (m) were 3–5 and 12 for the ground and ionosphere, respectively. We reexamine this event and attempt to explain why the spatial structure of the ULF wave in the ionosphere, seen by the Saskatoon Super Dual Auroral Radar Network (SuperDARN) radar, may differ from that deduced from the magnetometer data. The radar data are used to develop a two‐dimensional (2‐D) model of the spatial distribution of ULF amplitude and phase in the ionosphere. Our modeling shows that the differences between ground and ionosphere measurements may be explained by spatial integration. In general, m numbers deduced from ground measurements should be smaller than the ionospheric values, and they are strongly dependent on the ionospheric ULF amplitude and phase distribution in both latitude and longitude.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| 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".