Mapping ionospheric backscatter measured by the SuperDARN HF radars – Part 1: A new empirical virtual height model
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
Abstract. Accurately mapping the location of ionospheric backscatter targets (density irregularities) identified by the Super Dual Auroral Radar Network (SuperDARN) HF radars can be a major problem, particularly at far ranges for which the radio propagation paths are longer and more uncertain. Assessing and increasing the accuracy of the mapping of scattering locations is crucial for the measurement of two-dimensional velocity structures on the small and meso-scale, for which overlapping velocity measurements from two radars need to be combined, and for studies in which SuperDARN data are used in conjunction with measurements from other instruments. The co-ordinates of scattering locations are presently estimated using a combination of the measured range and a model virtual height, assuming a straight line virtual propagation path. By studying elevation angle of arrival information of backscatterred signals from 5 years of data (1997–2001) from the Saskatoon SuperDARN radar we have determined the actual distribution of the backscatter target locations in range-virtual height space. This has allowed the derivation of a new empirical virtual height model that allows for a more accurate mapping of the locations of backscatter targets.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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