Potential for issuing ionospheric warnings to Canadian users of marine DGPS
Bibliographic record
Abstract
Under normal operating conditions marine DGPS horizontal positioning accuracies on the order of several meters are achieved in North America. Degradations in positioning accuracy can occur during enhanced ionospheric activity. An ionospheric phenomenon known as storm enhanced density (SED) is observed to develop in the middle to high latitudes during ionospheric storm events. Very large gradients in total electron content are observed in the vicinity of this feature with DGPS positioning errors increased by a factor of 10–30 versus quiet conditions. The specific evolution of a given SED event and the magnitude of expected impact are not generally predictable. A method to monitor development of SED is to compute ionospheric maps in real time. Local gradients can then be computed for various geographic regions from North American maps of ionospheric delay. Sources of real‐time ionospheric information include the Wide Area Augmentation System (WAAS) and the Canadian GPS•C service. These are wide area differential GPS systems. In this paper, a real‐time ionospheric warning system is investigated for North American (primarily Canadian) DGPS users based on available real‐time data. The WAAS and GPS•C ionospheric models are inadequate to resolve ionospheric gradients for 100–200 km scale sizes. Raw GPS data from GPS•C reference sites can be used, however, to observe large ionospheric gradients and interpret the expected impact on DGPS users. Potential exists to issue marine user warnings based on this method. Results of this work can readily be extended to land DGPS applications, such as the NDGPS service in the United States.
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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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".