Machine Learning Methods Applied to Riometer Data Classification
Bibliographic record
Abstract
Humankind’s growing reliance on advanced technologies reveals potential vulnerability to space weather events caused by the Sun’s effects on Earth. Space weather events may cause disruption and damage to necessary technologies. Machine learning methods are being developed to predict and classify space weather events so precautions may be taken to mitigate the impact. This thesis explores the application of machine learning methods to previously recorded riometer (ground-based receivers used in monitoring ionospheric behavior) data. The University of Calgary’s Auroral Imaging Group has been overseeing a network of riometers since 1989. Riometer data, though plentiful, is noisy and subject to cosmic and terrestrial interference. Utilizing riometer data currently requires manual assessment of data by experts which takes a significant amount of time. The purpose of this thesis is to design a method to automate space weather data classification. In this thesis, data is selected from one riometer site to facilitate the development of preprocessing methods and machine learning model design. First, methods were developed to manage raw data and produce a filtered signal. Next, the preprocessed data was explored to produce various features describing the behavior of riometer data. Lastly, a neural net was designed and trained to classify data behavior. The trained model succeeds in the binary classification of riometer data.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".