Analysis of differential GPS performance for marine users during solar maximum
Bibliographic record
Abstract
GPS signals are refracted by the dispersive ionosphere, resulting in ranging errors dependent on both the given signal frequency and ionospheric total electron content. Such range errors translate into a degradation of positioning accuracies. While it is possible to mitigate the impact of ionospheric effects on GPS positioning applications through ionosphere modeling and/or differential techniques (DGPS), residual errors may persist in regions where steep gradients or localized irregularities in electron density exist, particularly during periods of high geomagnetic activity. Such effects are an issue for the reliable implementation of safety‐critical GPS systems. A solar maximum was observed in mid 2000 with associated degradations in GPS positioning accuracies. In this paper the impact of solar maximum on DGPS horizontal positioning applications is investigated. Analyses focus on determining limitations in horizontal positioning accuracies for operational marine DGPS systems. Long‐term analyses are conducted using data from permanent GPS reference networks in Canada, Brazil, and the United States. Several million observations are processed in this study during the years 1998–2000. Studies focus on large ionospheric gradients near the equatorial anomaly and at subauroral latitudes (associated with the main trough and storm‐enhanced densities). Results indicate that DGPS horizontal positioning accuracies are degraded by a factor of 2–5 relative to average values.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.001 | 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".