Assessment of ionosphere tomographic modeling performance using GPS data during the October 2003 geomagnetic storm event
Bibliographic record
Abstract
Precise ionospheric modeling is important for single‐frequency Global Positioning System (GPS) users to achieve optimal positioning accuracy because the ionospheric signal delay is now the largest error source for positioning and navigation with GPS. The ionospheric modeling during ionospheric storms is particularly critical since the signal delay may be higher than normal and may differ significantly from the broadcast ionosphere model (currently employed by single‐frequency users). In this study, a tomographic technique is used to model the ionosphere over North America using data collected from a network of dual‐frequency GPS receivers. In support of real‐time applications of the ionosphere model, short‐term (5‐min) forecasts of ionospheric total electron content (TEC) are also performed. To validate the accuracy of the forecast ionospheric TEC, a comparison of the TEC predictions with the observed TEC data (which are inferred from dual‐frequency GPS observations) is carried out. Analyses are conducted using GPS data recorded during a 2003 geomagnetic storm event (29–31 October). Results indicate that under less disturbed conditions, an average accuracy of 5 ∼ 6.5 total electron content units (TECU, 1 TECU = 1016 el m−2) can be obtained for the vertical TEC prediction and that 80% of slant TEC can be recovered by the model predictions. During extreme ionospheric storm periods (Kp = 9), the vertical TEC forecasting accuracy has a degradation of 2 ∼ 3 TECU from the 3‐day mean value, and the relative error is several percent to 10% larger than the 3‐day average level.
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 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 | 0.000 | 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 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".