SuperDARN Radar Software Toolkit (RST) 4.6
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
Key updates in version 4.6 of the Radar Software Toolkit (RST) include: Routine for removing non-gaussian noise/interference from fitacf files (<code>fit_speck_removal</code>) Routine to display the contents of old-format dat files (<code>datdump</code>) Shepherd (2017) elevation angle algorithm added to FITACF3.0 Ability to plot multiple fields of view with <code>fov_plot</code> Added missing <code>mlt2mlon</code> keyword to MLT_v2 IDL/DLM code <code>make_grid</code> detects and concatenates multiple input files automatically (deprecates <code>-c</code> flag) Check that the search noise is nonzero before using it to replace the skynoise in FITACF3.0 Check whether interferometer array is in front or behind main array when calculating <code>elv_low</code>/<code>elv_high</code> in FITACF2.5 Fixed bugs in plotting libraries, cdf file reading, <code>make_grid</code> and <code>trim_raw</code> Update hardware files for DCE and DCN, and PI institution information in <code>radar.dat</code> Improved compliance with GPLv3 license requirements Documentation updates The RST is actively developed and maintained by the SuperDARN Data Analysis Working Group (https://superdarn.github.io/dawg/).
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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.019 | 0.007 |
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