SuperDARN Radar Software Toolkit (RST) 4.3
Bibliographic record
Abstract
New features in version 4.3 of the Radar Software Toolkit (RST) include: Shepherd [2017] elevation angle algorithm Update the Heppner-Maynard boundary (HMB) lower latitude limit from 50 degrees to 40 degrees in map_addhmb Moved convection model software into a new library Moved elevation angle algorithms into a new library Bugfix to map_addimf for missing IMF values Updates to map, time, and field plotting Bugfix for negative phidiff for fitacf 3.0 Update to dmap to check for appropriate array size values Bugfix to IDL and DLM versions of FitClose functionality Bugfix to solve_model for longitude spacing for equal-area grid option Updates to real-time grid code in rtgrid Proper reading and writing of fit-level data file version numbers Updates to numerous hdw.dat files as well as radar.dat file Updates to documentation (includes a Readthedocs page that is still a work in progress in parts) Updates to make_smr output formatting Update to OldFitWrite library to store offset value Reduction in number of compilation warnings 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 | 0.010 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.006 | 0.004 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.153 | 0.155 |
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".