On the use of SuperDARN Ground Backscatter Measurements for Ionospheric Propagation Model Validation
Bibliographic record
Abstract
High-frequency (HF), radars can regularly see beyond the horizon, with this non-line-of-sight (LOS) propagation achieved through the use of the ionosphere as a reflector.The Super Dual Auroral Radar Network (SuperDARN) is a global network of HF coherent scatter radars operating in the range of 8-20 MHz and provides a vast data set of oblique HF soundings.Ground backscatter (GB) measurements present within this data have found increasing utility over time, showing use for interferometer calibration and real time determination of ionospheric parameters including fof2.We present a method for utilizing this vast data set to assess propagation models using two-dimensional numerical ray tracing to simulate the time evolution of ground backscatter echoes.Model and SuperDARN Leading Edge (LE) slant range is extracted and compared, showing errors of between 50and 300-km for the daytime IRI.Here we will comprehensively demonstrate and assess the utility of this data for validation.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".