Kinematic Post-processing of Ship Navigation Data Using Precise Point Positioning
Bibliographic record
Abstract
Seafloor geodetic studies such as Global Positioning System (GPS)-Acoustic experiments often require the measurement platform on the sea surface to be positioned accurately to within a few centimetres. In this paper, we test the utility of Precise Point Positioning (PPP) for this application with two experiments. The first fixed platform experiment is a comparison between three independent processing software packages: Positioning and Navigation Data Analyst (PANDA), Global Navigation Satellite System-Inferred Positioning System and Orbit Analysis Simulation Software (GIPSY-OASIS), and the Canadian Spatial Reference System (CSRS)) and a more accurate solution based on conventional differential processing of a remote GPS station in the Aleutian Islands. The second moving platform experiment is a comparison among the three PPP software packages using 40 hours of ship navigation data collected during theRoger RevelleRR1605 cruise 170 nautical miles southwest of Palau in May 2016. We found the PPP solutions were repeatable to 5·49 cm in the horizontal components and 12·4 cm in the vertical component. This demonstrates not only that PPP is a useful tool for positioning marine platforms in remote locations, but also that modern ship navigation instruments such as the Kongsberg Seapath 330 + are suitable for seafloor geodetic application.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".