Regional Computation of TEC using a Neural Network Model
Bibliographic record
Abstract
Ionospheric refraction is one of the most damaging effects on GPS signal. This effect is proportional to the total electron content (TEC), which is the number of free electrons contained in the ionospheric layer. Once the TEC is known, it is possible to determine the delay caused by the ionosphere on GPS signal. Due to the dispersive characteristic of the ionosphere, the delay is a function of the frequency. Using the observations of two frequencies of a GPS receiver it is possible to compute the TEC value for the local where the receiver is. Single frequency receiver users can use a regional model of TEC, generated by using data from a tracking network of dual frequency receivers. A network of receivers can generate a spatially distributed grid of TEC values. Using this grid it can be created a model from which is possible to estimate a TEC value to any position inside or near the region covered by the tracking network. Once the local TEC value is estimated, it is possible to correct the single frequency receiver observations. In this paper we present a new technique to regional TEC modelling, using a Neural Network approach. This new technique has the capability to predict TEC values derived from a GPS tracking network. Preliminary tests using the new technique indicate an accuracy in the TEC values estimation up to 98 %. In other words we can correct the ionospheric delay by the same amount, due to its direct relationship with TEC.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".