Topside Ionogram Scaler With True Height Algorithm (TOPIST): Automated processing of ISIS topside ionograms
Bibliographic record
Abstract
The United States/Canadian ISIS‐1 and ISIS‐2 satellites collected several million topside ionograms in the 1960s and 1970s with a multinational network of ground stations that provided good global coverage. However, processing of these ionograms into electron density profiles required time‐consuming manual scaling of the traces from the analog ionograms, and as a result, only a few percent of the ionograms had been processed into electron density profiles. In recent years an effort began to digitize the analog recordings to prepare the ionograms for computerized analysis. As of November 2002, approximately 390,000 ISIS‐1 and ISIS‐2 digital topside‐sounder ionograms have been produced. The Topside Ionogram Scaler With True Height Algorithm (TOPIST) program was developed for the automated scaling of the echo traces and for the inversion of these traces into topside electron density profiles. The program is based on the techniques that have been successfully applied in the analysis of ground‐based Digisonde ionograms. The TOPIST software also includes an “editing option” for manual scaling of the more difficult ionograms, which could not be scaled during the automated TOPIST run. TOPIST is now successfully scaling ∼60% of the ISIS ionograms, and the electron density profiles are available through the online archive of the National Space Science Data Center at ftp://nssdcftp.gsfc.nasa.gov/spacecraft_data/isis/topside_sounder . This data restoration effort is producing a unique global database of topside electron densities over more than one solar cycle, which will be of particular importance for improvements of topside ionosphere models, especially the International Reference Ionosphere.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.019 | 0.007 |
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".