Evaluation of Ionospheric Interpolation Algorithms for Regional and National GPS Networks in Canada
Bibliographic record
Abstract
The purpose of this paper is to determine a 2-dimensional ionosphere model, applicable in real-time, that provides optimal accuracy of vertical delays at ionospheric grid points (IGPs). This model is based on GPS delays measured at ionospheric pierce points (IPPs), as observed from dual-frequency GPS tracking stations. Two algorithms are selected for possible implementation: spherical harmonic model and thin plate spline interpolation. These methods are based on twodimensional estimation on an ionospheric shell at 350 km altitude. The input observations are computed as slant delays using dual frequency GPS observations. In the spherical harmonics model, the coefficients and receiver differential code biases are estimated every 5 minutes in a real-time mode using a Kalman filter. Thinplate spline is a 2-dimensional generalization of the cubic spline in 1 dimension. The basic idea of this method is to build a function which passes through the grid points and minimizes the roughness of the surface. The performance of the two algorithms is evaluated in terms of accuracy of the residuals between observed vertical TECs (VTECs) and estimated VTECs at IGPs in two GPS networks: Canadian Active Control System (CACS) and Western Canada Deformation Array (WCDA). The impact of ionospheric conditions on each algorithm is also assessed by processing data from the period May 28–May 31, 2003 when a severe geomagnetic storm occurred. The spatial and temporal variation of the geomagnetic storm is also investigated by plotting vertical TEC maps over Canada.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".