Seasonal variation of HF radar <i>F</i> region echo occurrence in the midnight sector
Bibliographic record
Abstract
Long‐term data (1996–2001) for a number of Super Dual Auroral Radar Network (SuperDARN) HF radars in both the Northern and Southern Hemispheres are used to study the midnight F region echo occurrence. We confirm the previously reported increase of echo occurrence toward the solar cycle maximum for all radars considered and a clear winter maximum for some of them. The echo occurrence rate experiences clear equinoctial maxima for many radar locations, especially at higher latitudes and in Antarctica. We attribute the solar cycle echo increase in the midnight sector to the more frequent occurrence of enhanced electric fields and strong plasma density gradients. The equinoctial maxima are believed to be controlled entirely by the electric field increase due both to the Russell‐McPherron effect and to differences in conjugate ionospheric conductances controlled by the tilt of the Earth's axis. For the low geographic latitude portion of the Saskatoon radar observations, the echo statistics differ from the other radars; there is a clear summer maximum in echo occurrence and no definite signature of equinoctial maxima. A summer maximum in low‐latitude echo occurrence also is observed by the Hankasalmi radar during the solar cycle minima. The effect is attributed to improved propagation conditions for HF radio waves during summer periods for the latitudes where, for other seasons, there is a deficiency in the electron density.
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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 | 0.001 |
| 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) | 0.001 | 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".