<i>F</i> region ionosphere effects on the mapping accuracy of SuperDARN HF radar echoes
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 Structured particle precipitation in the cusp is an important source for the generation of F region ionospheric irregularities. The equatorward boundaries of broad Doppler spectral width in Super Dual Auroral Radar Network (SuperDARN) data and the concurrent OI 630.0 nm auroral emission are good empirical proxies for the dayside open‐closed field line boundary. However, SuperDARN currently employs a simple virtual model to determine the location of its echoes, instead of a direct calculation of the radio wave path. The varying ionospheric conditions could influence the final mapping accuracy of SuperDARN echoes. A statistical comparison of the offsets between the SuperDARN Finland radar spectral width boundary (SWB) and the OI 630.0 nm auroral emission boundary (AEB) from a meridian‐scanning photometer (MSP) on Svalbard is performed in this paper. By restricting the location of the 630.0 nm data to be near local zenith where the MSP has the highest spatial resolution, the optical mapping errors were significantly reduced. The variation of the SWB‐AEB offset confirms that there is a close relationship between the mapping accuracy of the HF radar echoes and solar activity. The asymmetric variation of the SWB‐AEB offset versus magnetic local time suggests that the intake of high‐density solar extreme ultraviolet ionized plasma from postnoon at subauroral latitudes could result in a stronger refraction of the HF radar signals in the noon sector, while changing the HF radar operating frequency also has a refraction effect that contributes to the final location of the HF radar echoes.
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 | 0.001 | 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