The Low-latitutde ionosphere: monitoring its behaviour with GPS
Bibliographic record
Abstract
Since the late 1980’s various research groups have been investigating the behaviour of the ionosphere using Global Positioning System (GPS) data. These investigations are based on the total electron content (TEC) measurements derived from dual-frequency GPS observations taking advantage of the dispersive nature of the ionospheric medium. Currently, there is a large number of GPS receivers in continuous operation worldwide. Even though large in number, these stations are unevenly distributed, being situated mostly in the northern hemisphere region. The relatively smaller number of GPS receivers in the southern hemisphere, and consequently the reduced number of available TEC measurements, causes ionospheric modelling to be less accurate for this region. GPS data from the Brazilian Network for Continuous Monitoring by GPS (RBMC) have been used for the first time to obtain TEC values in order to monitor the ionospheric behaviour in the South American region. For this task, we are using the University of New Brunswick (UNB) Ionospheric Modelling Technique which uses a spatial linear approximation of the vertical TEC above each station using stochastic parameters in a Kalman filter estimation to describe the local time and geomagnetic latitude dependence of the TEC. The utilisation of the RBMC GPS data to monitor the ionosphere over South America can help us to obtain a better understanding of many important low latitude ionospheric phenomena, such as the Appleton Equatorial Anomaly and the South Atlantic Anomaly as well as more accurate and representative regional and global ionospheric models. Furthermore, the effect of geomagnetic storms on the equatorial and low-latitude ionosphere is discussed, as well as the integrity of GPS data obtained in equatorial and low-latitude regions.
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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 | 0.001 | 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) | 0.000 | 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 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".