Assessment Study of Using Online (CSRS) GPS-PPP Service for Mapping Applications in Egypt
Bibliographic record
Abstract
Assessment Study of Using Online (CSRS) GPS-PPP Service for Mapping Applications in Egypt Many applications in navigation, land surveying, land title definitions and mapping have been made simpler and more precise due to accessibility of Global Positioning System (GPS) data, and thus the demand for using advanced GPS techniques in surveying applications has become essential. The differential technique was the only source of accurate positioning for many years, and remained in use despite of its cost. The precise point positioning (PPP) technique is a viable alternative to the differential positioning method in which a user with a single receiver can attain positioning accuracy at the centimeter or decimeter scale. In recent years, many organizations introduced online (GPS-PPP) processing services capable of determining accurate geocentric positions using GPS observations. These services provide the user with receiver coordinates in free and unlimited access formats via the internet. This paper investigates the accuracy of the Canadian Spatial Reference System (CSRS) Precise Point Positioning (PPP) (CSRS-PPP) service supervised by the Geodetic Survey Division (GSD), Canada. Single frequency static GPS observations have been collected at three points covering time spans of 60, 90 and 120 minutes. These three observed sites form baselines of 1.6, 7, and 10 km, respectively. In order to assess the CSRS-PPP accuracy, the discrepancies between the CSRS-PPP estimates and the regular differential GPS solutions were computed. The obtained results illustrate that the PPP produces a horizontal error at the scale of a few decimeters; this is accurate enough to serve many mapping applications in developing countries with a savings in both cost and experienced labor.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".