General resource for ionospheric transient investigations (GRITI): An open-source code developed in support of the Dinsmore et al. (2021) results
Bibliographic record
Abstract
The analysis techniques and the corresponding software suite GRITI (General Resource for Ionospheric Transient Investigations) are described. GRITI was used to develop the Dinsmore et al. [2] results, which found a novel classification of traveling ionospheric disturbances (TIDs) called semi-coherent ionospheric pulsing structures (SCIPS). The any-geographic range (local-to-global), any-azimuth angle keogram algorithm used to analyze SCIPS in that work is detailed. The keogram algorithm in GRITI is applied to detrended vTEC (vertical Total Electron Content) data, called delta-vTEC herein, in Dinsmore et al. [2] and the follow-on paper Dinsmore et al. [3], but is also applicable to any other two-dimensional dataset that evolves through time. GRITI's delta-vTEC processing algorithm is also described in detail, which is used to provide the delta-vTEC data for Dinsmore et al. [3]. We detail a keogram algorithm for analysis of delta-vTEC data in Dinsmore et al. [2] and the follow-on paper Dinsmore et al. [3]. We detail a delta-vTEC processing algorithm that converts vTEC data to delta-vTEC through detrending that is used to provide the delta-vTEC data used in Dinsmore et al. [3]. GRITI is an open-source Python 3 analysis codebase that encompasses the delta-vTEC processing and keogram algorithms. GRITI has additional support for other data sources and is designed for flexibility in adding new data sources and analysis methods. GRITI is available for download at: https://github.com/dinsmoro/GRITI.
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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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.059 | 0.074 |
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".