Comparative Evaluation of GNSS Positioning Accuracy Using RTK Techniques and CORS-Based Post-Processing Solutions
Bibliographic record
Abstract
Global Navigation Satellite System (GNSS) applications in Osun State, Nigeria, are limited by poor positional accuracy, particularly in vertical measurements. With the recent establishment of a Continuously Operating Reference Station (CORS) by private surveyors, it is important to assess the performance of real-time and post-processed GNSS solutions for local survey practice. This study compared two real-time kinematic (RTK) connection methods: radio frequency (RF) and network RTK via internet (NRTK) with static precise positioning using Canadian Spatial Reference System Precise Point Positioning (CSRS-PPP) and Osun CORS RINEX data processed in South Geomatics Office software. Field data were collected on three control stations with a Tersus Oscar GNSS receiver, and accuracy was evaluated using root mean square error (RMSE) against published control coordinates. Results show that CSRS-PPP (0.11 m easting, 0.17 m northing, 3.78 m height) and Osun CORS (0.09 m easting, 0.11 m northing, 3.76 m height) achieved horizontal accuracies within the allowable 0.05-0.50 m limit, but vertical errors exceeded the 0.10 m tolerance. NRTK (0.06 m easting, 0.03 m northing) performed better than RF-RTK (0.53 m easting, 1.29 m northing) for horizontal positioning, yet both produced unacceptable vertical errors of 24.14 m and 8.54 m, respectively. The findings confirm that online PPP and internet-based RTK provide reliable horizontal accuracy in Osun State, but vertical accuracy remains inadequate and requires further methodological improvement.
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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 | 0.004 |
| 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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".