Development and Evaluation of a New 3-D Ionospheric Modeling Method
Why this work is in the frame
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Bibliographic record
Abstract
ABSTRACT: Described in this paper is a new three-dimensional (3-D) ionospheric modeling method developed on the basis of tomographic techniques and GPS network data. This model is constructed with spherical harmonic and empirical orthogonal functions. The methods for constructing the model and evaluating its performance are first developed. Performance assessment results are then presented using datasets from two different types of GPS reference networks. Performance is evaluated in terms of ionospheric total electron content (TEC) prediction accuracy and the impact on improving single-point positioning accuracy. Results indicate that short-term (5 min) predictions can typically recover over 95 percent of the total ionospheric errors when modeling a local-area GPS network under medium ionospheric activity conditions. Under extremely active ionospheric conditions (Kp = 8–9), recovery can achieve an average level of about 72 percent using data from a wide-area GPS network.
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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 | 0.001 | 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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it