Performance of satellite‐based navigation for marine users during ionospheric disturbances
Bibliographic record
Abstract
The Global Positioning System (GPS) is used worldwide for marine navigation in support of hydrographic surveying operations, where horizontal positioning requirements are typically better than 10 m (95%). Differential techniques are used to reduce errors associated with signal propagation through the dispersive ionosphere and to achieve such accuracies. Two such methods are differential GPS (DGPS), in which range corrections are derived for a nearby reference station and are applied at the remote user location, and wide‐area differential GPS (WADGPS), in which ionosphere range errors are modeled over a large area using a sparse network of GPS ground stations. Both DGPS and WADGPS (Wide Area Augmentation Service) positioning accuracies are investigated here, throughout North America, for a severe geomagnetic storm event in 2003. DGPS horizontal positioning errors of 10–15 m (95%) are observed during this event, and WADGPS positioning errors generally exceed those for DGPS. This is attributed to the sparse WADGPS reference network and associated limitations in resolving the severe ionosphere gradients.
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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 | 0.003 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".