Taiwan Online Precise Point Positioning Service: Methodology and Test Results
Bibliographic record
Abstract
Online precise point positioning (PPP) computation services can bring substantial benefits to surveying engineering and its applications. This study introduces Taiwan’s first online PPP service, named Taiwan Online Precise Point Positioning Service (TOPS). TOPS uses a between-satellite single-differenced (BSSD) PPP model to process dual-frequency Global Positioning System (GPS) and Globalnaya Navigatsionnaya Sputnikovaya Sistema (GLONASS) measurements. Moreover, this study proposes a method of geodetic datum transformation from the International Terrestrial Reference Frame (ITRF) to Taiwan Geodetic Datum 1997 at epoch 2010.0 (denoted as TWD97[2010]), which is one of Taiwan’s official coordinate systems. The positioning performance of TOPS was tested. Results demonstrated that in terms of daily solutions, TOPS can provide accuracy comparable with that of other online services such as the Canadian Spatial Reference System Precise Point Positioning (CSRS-PPP). The positioning results were transformed from ITRF to TWD97[2010] and compared with predetermined fiducial coordinates, and the results indicated that although the largest discrepancy in the horizontal direction was about 5 cm, the overall accuracy of the transformation can satisfy the requirement of horizontal control surveys in Taiwan.
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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 | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".