SuperDARN Radar Software Toolkit (RST) 4.2
Bibliographic record
Abstract
New features in version 4.2 of the Radar Software Toolkit (RST) include: TS18 and TS18-Kp statistical convection model (TS18 replacing CS10 as default) Statistical convection model solver (solve_model) Added pre-1900 coefficients for use with AACGM_v2 Implemented new --version option to print current RST version number Added unrecognized command line option handling to remaining binaries Disabled low-power thresholding when converting between dat and rawacf format files Fixed bug when reading data from early (~1993-1995) dat format files Fixed bug when reading records with negative scan flag values Fixed bugs in gridtogrdmap and maptocnvmap conversion routines Fixed bug in trim_fit to allow removal of first record in file Fixed bug in map_addhmb when specifying custom boundary latitude Fixed bug in make_grid preventing custom start times with -i flag Fixed bugs in AACGM_v2 software Fixed numerous plotting bugs Updated Falkland Islands and Stokkseyri hdw.dat files Updated README and documentation for FITACF 3.0 Updated documentation following the -new/-old flag change from previous release (4.1) Cleanup of numerous warning messages during compilation 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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.006 | 0.004 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.163 | 0.179 |
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".