Possible generation mechanisms for Pc1 pearl structures in the ionosphere based on 6 years of ground observations in Canada, Russia, and Japan
Bibliographic record
Abstract
Abstract We investigate pearl structures (amplitude modulations) of Pc1 pulsations simultaneously observed at Athabasca (ATH, 54.7°N, 246.7°E, L = 4.3) in Canada, Magadan (MGD, 60.1°N, 150.7°E, L = 2.6) in Russia, and Moshiri (MOS, 44.4°N, 142.3°E, L = 1.5) in Japan. From 6 years of ground observations, from 2008 to 2013, we selected 84 Pc1 events observed simultaneously at the longitudinally separated stations (ATH and MGD) and 370 events observed at the latitudinally separated stations (MGD and MOS), all with high coherence (>0.7) of Pc1 waveforms. We calculated the cross‐correlation coefficient (similarity: r) for the Pc1 pearl structures and found that more than half of the events in both pairs had low similarity (r < 0.7), indicating that most Pc1 waves exhibit different pearl structures at different stations. We found that high‐similarity Pc1 pearl structures (r > 0.7) at the longitudinally separated stations are concentrated from 6 to 15 UT when both stations are in the nighttime. The similarity of Pc1 pearl structures tends to show a negative correlation with the standard deviation of the polarization angle in both pairs. The observed repetition period of Pc1 pearl structures has a clear positive correlation with the repetition period estimated from Pc1 bandwidth by assuming beating of different frequencies. From these results, we suggest that ionospheric beating effect could be a dominant process for the generation of Pc1 pearl structures. Beating processes in the ionosphere with a spatially distributed ionospheric source can cause the different shapes of Pc1 pearl structures at different observation points during ionospheric duct propagation.
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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 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".