Optimal <i>F</i> Region Electron Density for the PolarDARN Radar Echo Detection Near the Resolute Bay Zenith
Bibliographic record
Abstract
Abstract Operation of over‐the‐horizon radars comprising the Super Dual Auroral Radar Network (SuperDARN) relies on strong ionospheric refraction of high‐frequency (HF, f = 10–15 MHz) radiowaves such that in order to provide reliable coverage of a given geographic location, the working frequency has to be optimized with respect to the ionospheric conditions. In this work, joint observations of the Rankin Inlet, Inuvik, and Clyde River PolarDARN/SuperDARN HF radars near the Resolute Bay (RB) zenith, where the incoherent scatter radars that monitored the electron density distribution in the ionosphere, are used to assess the F region peak electron density required for HF echo detection near the RB zenith. We show that the echo occurrence rate increases with the electron density up to Ne ≈ (2 − 3) ⋅ 1011 m−3, and above this value, the occurrence rate saturates. Thus, optimum electron density for echo detection exist for every HF radar. The saturation effect is more pronounced for the Clyde River radar whose ranges of echo detection are smallest. The saturation in the dependence is reached at smaller densities for the Inuvik radar whose location is farthest from RB and for lower transmitting frequency of observations for every radar. The data presented suggest that having strong‐enough electron density in the ionosphere is the major factor for detection of HF echoes in winter or equinox.
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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 | 0.000 | 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".