Congruence through repeatability of position solutions by different GNSS survey techniques
Bibliographic record
Abstract
In this study, we determined three-dimensional (3D) position coordinates for eight new Continuous Operating Reference Stations (CORS) in Ghana through three different GNSS positioning techniques. The three GNSS positioning techniques whereby the network of CORS was tied to ITRF14 and War Office 1926 datums included:1) Precise Point Positioning (PPP); 2) Precise Differential GNSS (PDGNSS), using reference stations based on ITRF14; and 3) PDGNSS, using reference stations based on War Office. The PPP solutions were computed using the Canadian Spatial Reference System Precise Point Positioning software (CSRS-PPP), available online and as an open source GNSS laboratory tool software (gLAB). The PDGNSS solutions were obtained from OPUS and AUSPOS online services, as well as from self-post-processing using Topcon Tools software v8.2.3. All solutions were computed using 24-hour data for twelve consecutive days in the month of October 2018 (GPS DoY 284 to GPS DoY 295). The quality, reliability, and acceptability of position solutions were measured by computing the average positioning error, the rate of ambiguity resolution and the repeatability ratios of the solutions. The variability of coordinate differences for each pair of different positioning techniques was computed to determine their solution congruences. Ultimately, , the average positioning errors in northing, easting, and height were 0.003m, 0.005m and 0.009m, respectively. The rate of ambiguity resolution was between 75.3% and 90.3%. Repeatability ratios ranged between 1: 68,500,000 and 1: 411,100,000. Finally, the minimum and maximum range of variability in coordinate differences for each pair of positioning techniques was 1mm to 16mm for horizontal positions and 2mm to 137mm for vertical positions.
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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 | 0.005 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".