Ionospheric Electron Density and Temperature Profiles Using Ionosonde-Like Data and Machine Learning
Bibliographic record
Abstract
Predicting the behaviour of the Earth's ionosphere is crucial for ground-based and space borne technologies relying on it. This paper presents a novel way of inferring the ionospheric electron density profiles and electron temperature profiles using machine learning. The analysis is based on the Nearest Neighbor (NNB) and Radial Basis Function (RBF) regression models. Synthetic data sets used to train and validate these two inference models are constructed using the International Reference Ionosphere (IRI 2020) model with randomly chosen years (1987-2022), months (1-12), days (1-31), latitudes (-60 to 60°), longitudes (0, 360°), times (0-23h), at altitudes ranging from 95 to 600 kilometres. The NNB and RBF models use the constructed ionosonde-like profiles to infer complete ISR-like profiles. The results presented show that the inference of ionospheric electron density profiles is better with the NNB model than with the RBF model while the RBF model is better at inferring the electron temperature profiles than the NNB model. A major and unexpected finding of this research is the ability of the two models in inferring full electron temperature profiles that are not provided by ionosondes using the same truncated electron density dataset used to infer electron density profiles. NNB and RBF models generally overestimate or underestimate the inferred electron density and electron temperature values, especially at higher altitudes, but they tend to produce good matches at lower altitudes.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".