Climatology of HF Propagation Characteristics at Very High Latitudes From SuperDARN Observations
Bibliographic record
Abstract
Abstract Conventional forecasting of high‐frequency (HF, 3–30 MHz) radio wave propagation is based on a combination of ionospheric and propagation models. However, at very high latitudes this approach is seriously undermined by the intrinsically dynamic ionospheric conditions regularly perturbed by energetic particle precipitations and strong electric fields. From this perspective, the multi‐year observations of HF propagation characteristics by Super Dual Auroral Radar Network (SuperDARN) radars across auroral and polar cap regions represent a unique opportunity for systematic validation of the conventional approach, as well as for creating an empirical propagation model directly from the radar observations. Qualitative identification and quantitative characterization of the propagation modes requires an accurate knowledge of the vertical angle of arrival (elevation angle) across the high‐latitude part of the radar network. This information has become available only in recent years, facilitated by the development of reliable data‐based calibration techniques for SuperDARN interferometry. We present the solar‐cycle/seasonal/diurnal climatology of HF propagation characteristics at very high latitudes derived from two‐frequency observations by the Rankin Inlet SuperDARN radar.
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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 | 0.000 |
| 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) | 0.001 | 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".