SuperDARN Radar Software Toolkit (RST) 5.0
Bibliographic record
Abstract
Key updates in version 5.0 of the Radar Software Toolkit (RST) include: All fitting algorithms are now called by the make_fit binary, and the following binaries have been removed: make_lmfit, make_fitex2, make_fitex1. There is no default ACF fitting algorithm in make_fit. The user should specify the fitting algorithm using a command line option. Added the LMFit2.0 fitting algorithm C & IDL software to read TDIFF values from a calibration file (sample file included) New fields in the fitacf file format: algorithm, tdiff, elv_error and elv_fitted New command line option in map_addhmb to constrain the HMB based on a spectral width threshold (no change to default behavior) New virtual height model for midlatitude radars and modularized rpos_v2 library Speed improvements to map_grd and map_addhmb (~3x) New GUI for displaying real-time fit data (fitacfclientgui binary) Updates to the data simulator (sim_real and make_sim) New command line options in field_plot to correctly display the radar field of view Improved color control in plotting routines Updated documentation
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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 | 0.003 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.006 | 0.003 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.139 | 0.127 |
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".