Statistical Analysis of Off‐Great Circle Radio Wave Propagation in the Polar Cap
Bibliographic record
Abstract
Abstract High latitude ionospheric density structures such as polar cap patches and arcs are capable of deflecting high frequency (HF) radio waves to off‐great circle paths, and are likely detrimental to technologies dependent on HF radio propagation. In this study, nearly 2.5 years of 4.6–14.4 MHz data from a multi‐frequency HF radio link between Qaanaaq, Greenland and Alert, Canada are used to investigate high‐latitude off‐great circle propagation in the polar cap. After an example of HF radio propagation affected by polar cap patches is shown in detail, a statistical analysis of the occurrence and impacts of off‐great circle deflections in the polar cap is presented. Off‐great circle propagation is shown to be increasingly common with increasing frequency up to 11.1 MHz, such that averaged over 1 year, received 11.1 MHz signals experienced deflections >30° from the great circle direction 65.6% of the time. The occurrence of these deflections across the year is shown to be at a maximum in the winter, while occurrence across the day varies with season. Trends across both time of day and time of year for 11.1 and 14.4 MHz deflections are consistent with polar cap patch occurrence trends. Off‐great circle deflections are shown to be associated with increased time‐of‐flights, a larger range of positive and negative Doppler shifts, increased Doppler spreads, and lower signal‐to‐noise ratios. These results are discussed in the context of ionospheric phenomena in the polar cap, and implications for over‐the‐horizon radars operating at high latitudes.
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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 | 0.003 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".