Phase and Amplitude Scintillations Associated With Polar Cap Patches: Statistical and Event Analyses
Bibliographic record
Abstract
Abstract Ionospheric scintillation causes errors in radio signals. One potential source in the polar region is polar cap patches. Using a polar cap patch database from Ren et al. (2018), https://doi.org/10.1029/2018ja025621 and data provided by a scintillation receiver at Resolute Bay, we evaluated the polar cap patch impact on ionospheric scintillation. In a statistical analysis, we found that 92% of the maximum patch scintillation was under 0.3 for both phase (in the unit of radian) and amplitude scintillations. Of the remaining 8%, less than 1% could be classified as severe phase scintillation (>0.5 radians), while none could be classified as severe amplitude scintillation. Magnetic Local Time (MLT) dependence of the patch phase scintillation shows higher scintillation values near noon MLTs. Patches are classified into cold, hot‐Te and hot‐Te‐Ti types based on their plasma temperatures. The dependence of scintillation on the patch density prominence showed a positive correlation between phase scintillation and cold patches near noon MLT and hot‐Te patches everywhere except midnight MLT. In the event analysis, two events were studied. The first event with large phase scintillation was located in the polar cap with weak polar rain precipitation and large, uniform, antisunward convection flows. The second event with large amplitude scintillation was located in a region with active soft particle precipitation and velocity shear, classical ionospheric signatures of plasma sheet boundary layer. The statistical and event analysis improve understanding of the location and characteristics of patch‐related scintillations.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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 | 0.001 |
| 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 teacher head, 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".