Diurnal and seasonal behavior of the Hokkaido East SuperDARN ground backscatter: simulation and observation
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Bibliographic record
Abstract
We studied regular diurnal and seasonal behaviors of ground backscatter propagation characteristics corresponding to the Hokkaido East Super Dual Auroral Radar Network (SuperDARN) (43.53° N, 143.61° E). Firstly, we simulated key propagation characteristics using a high frequency (HF) calculation technique based on the waveguide approach and International Reference Ionosphere (IRI)-2012 model as background ionosphere. The minimum slant range, skip distance, corresponding elevation angle, and true reflection height were considered in this study. The behaviors of these characteristics were well explained by diurnal and seasonal variations in the critical frequency and maximum height of corresponding ionosphere layer in HF reflection point. We estimated the accuracy of the standard SuperDARN mapping technique and proposed a means for its improvement. Secondly, we constructed an algorithm for mass data processing and extracted diurnal dependencies of the minimum slant range, corresponding elevation angle, and effective reflection height from the Hokkaido East SuperDARN dataset for a period from 2007 to 2014. The algorithm uses the simulated characteristics for distinguishing regular ground backscatter echoes propagating in the E and F2 HF channels. Observed monthly mean and simulated values of the characteristics were compared, and the result showed that the accuracy of IRI-2012 significantly depends on solar activity level and orientation of HF propagation path. In general, the difference between observed and simulated values decreased with increases in solar activity and azimuth. We also analyzed the occurrence of echoes originating behind the radar and found that they most frequently appear in winter and equinoxes before sunrise in beam #0 and after sunset in beam #15. The probability of their observation for a specific local time could reach up to 35 %.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 it