Characterization of ionospheric scintillation variations in the polar region
Bibliographic record
Abstract
Ionospheric scintillation is a rapid change in the amplitude and phase of GNSS signals triggered by ionospheric irregularities, and it is an important indicator reflecting space weather effects and the intensity of ionospheric disturbances. An in-depth understanding of its spatial and temporal evolution characteristics is of great significance for improving the reliability of GNSS positioning as well as spatial disturbance forecasting. This paper systematically analyzes the evolution characteristics of scintillation under different solar activity intensities based on multi-year observation data from four stations of the CHAIN network in the Canadian polar region. The results show that: (1) scintillation is mainly concentrated in the evening to midnight, with a rapid decay before dawn; (2) the activity is significant during the winter and the spring/autumn equinoxes, driven by particle settling and magnetotail reconnection; (3) the scintillation probability gradually decreases from high latitude to low latitude, presenting a clear transition structure; (4) the scintillation intensity shows a significant positive correlation with the solar activity in terms of the inter-annual variations, with the most active years of the Sun (e.g., 2024) showing more frequent and strong perturbations. more frequent and strong perturbation phenomena. The results contribute to a deeper understanding of the dynamics of the irregular structure of the polar ionosphere and its driving mechanism.
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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 | 0.002 |
| 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".