SuperDARN Radar Software Toolkit (RST) 5.0
Bibliographic record
Abstract
Key updates in version 5.0 of the Radar Software Toolkit (RST) include: <strong>All fitting algorithms are now called by the <code>make_fit</code> binary</strong>, and the following binaries have been removed: <code>make_lmfit</code>, <code>make_fitex2</code>, <code>make_fitex1</code>. There is <strong>no default ACF fitting algorithm</strong> in <code>make_fit</code>. 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 <code>fitacf</code> file format: <code>algorithm</code>, <code>tdiff</code>, <code>elv_error</code> and <code>elv_fitted</code> New command line option in <code>map_addhmb</code> to constrain the HMB based on a spectral width threshold (no change to default behavior) New virtual height model for midlatitude radars and modularized <code>rpos_v2</code> library Speed improvements to <code>map_grd</code> and <code>map_addhmb</code> (~3x) New GUI for displaying real-time fit data (<code>fitacfclientgui</code> binary) Updates to the data simulator (<code>sim_real</code> and <code>make_sim</code>) New command line options in <code>field_plot</code> 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 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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.847 | 0.495 |
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; both teacher heads agree on what is shown here.
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".