A long-term study of the variability of polar-cap patches using Advanced Modular Incoherent Scatter Radars (AMISRs)
Bibliographic record
Abstract
The polar cap ionosphere is a dynamic and intricately structured environment that plays host to polar cap patches and other mesoscale density formations. These phenomena can lead to the emergence of smaller-scale structures through various plasma instability mechanisms. Existing literature highlights substantial variability in the occurrence, density, and characteristics of polar cap patches (PCPs) influenced by solar and geomagnetic conditions. However, a comprehensive statistical analysis utilizing long-term data is lacking, particularly from the Resolute Bay Incoherent Scatter Radar North (RISR-N). In this article, we consider 11-years of RISR-N data (≅one solar cycle) to perform an analysis of polar cap patches, focusing on their occurrence distributions, behavior with density, temperature, different geomagnetic indices and their characteristics over time. We show the long-term distribution of PCPs and how it varies with geomagnetic activity. We examine the role of solar activity by investigating correlations between solar activity indices (e.g., F10.7, solar wind conditions) and the occurrence of PCPs to provide a clearer picture of the influence of solar activity on patch dynamics. We identify seasonal and diurnal variability of PCPs to establish a clear understanding of how these factors influence their behavior.
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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) | 0.000 | 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".