Comparison of Total Electron Content (TEC) Maps over Brazil from Different Sources
Bibliographic record
Abstract
Total Electron Content (TEC) allows to evaluate the state of the ionosphere. Radio waves like GNSS signals traversing the ionosphere suffer delays and refraction. Ionospheric plasma irregularities may be generated in the equatorial regions after sunset and extend to low latitudes forming large plasma depleted regions named ionospheric bubbles. Signature of these bubbles can be observed at TEC maps. Inside plasma bubbles smaller scale size irregularities are generated causing scintillation in GNSS signals. This work compared TEC maps from some sources, with different temporal and spatial resolutions/coverage. Significant differences were found. For each source, there are differences in the treatment and preprocessing of raw data in order to get the absolute TEC values, which are interpolated to get grid values of the map. Even for the same source there are significant differences in the density of monitoring stations according to the region. A case of study concerning scintillation is also analyzed using the corresponding TEC and scintillation maps. TEC maps employed here encompass the years from 2022 to 2024, in the growing phase of the current solar cycle 25. The months of March, June, September and December were selected to take into account the TEC seasonal variation.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".