On the use of SuperDARN Ground Backscatter Measurements for Ionospheric Propagation Model Validation
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 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 it