SuperDARN Radar Software Toolkit (RST) 4.6
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 (fit_speck_removal) Routine to display the contents of old-format dat files (datdump) Shepherd (2017) elevation angle algorithm added to FITACF3.0 Ability to plot multiple fields of view with fov_plot Added missing mlt2mlon keyword to MLT_v2 IDL/DLM code make_grid detects and concatenates multiple input files automatically (deprecates -c 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 elv_low/elv_high in FITACF2.5 Fixed bugs in plotting libraries, cdf file reading, make_grid and trim_raw Update hardware files for DCE and DCN, and PI institution information in radar.dat 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/).
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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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.007 | 0.004 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.179 | 0.169 |
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".