Usability of the GPS Precise Point Positioning Technique in Marine Applications
Bibliographic record
Abstract
This study investigates the accuracy of an online Precise Point Positioning (PPP) service operated by the Geodetic Survey Division of Natural Resources Canada (NRCan), Canadian Spatial Reference System (CSRS)-PPP, by using single/dual-frequency Global Positioning System (GPS) data collected by dual-frequency geodetic-grade and Original Equipment Manufacturer (OEM) board type single-frequency GPS receivers. In this work, a kinematic test was carried out in Halic Bay (Golden Horn), Istanbul, Turkey, to assess the performance of the PPP method in a dynamic environment. Based on this study, it can be concluded that the coordinates estimated from the online CSRS-PPP service have a potential of about metre-level accuracy by processing single frequency data collected by an OEM receiver and about a decimetre to a few centimetres level accuracy by processing dual frequency data collected by a geodetic-grade receiver. In general, results show that the PPP technique has become a significant alternative to the conventional relative (differential) positioning techniques (i.e., Differential GPS (DGPS), Real-time Kinematic (RTK)). The technique does not suffer from the drawbacks of the DGPS technique and has potential to provide the same position accuracy without the requirement for a reference station. Consequently, it has been concluded that the PPP technique may be effectively used in marine applications due to its ease of use and provision of high accuracy, as well as being able to offer reduced field operational costs.
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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 | 0.004 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".