A Novel Rapid GNSS Network Solution in Mountainous Region Monitoring considering the Tropospheric Delay at Ground Points
Bibliographic record
Abstract
Due to the short peak observation time of global navigation satellite systems (GNSS), the accuracy of the tropospheric delay estimation and the positioning are poor. In this study, a rapid GNSS network solution for mountainous regions is presented. The high‐precision tropospheric delay at ground points is obtained from long‐term ground observation data and used as a priori constraint in the double‐difference equation of short‐time synchronous peak observations to realize rapid and high‐precision positioning. Chinese mountain survey networks with large elevation gradient (1000~2000 m) were selected for the experimental verification of the proposed method. The results show that the rapid peak positioning method weakened the effect of the residual tropospheric delay caused by the elevation difference, significantly improving the accuracy and reliability of the results. The positioning accuracy of the peak in upward direction was better than 1.1 cm, which meets the requirements of rapid short‐span (~1 h) high‐precision monitoring and achieves 24 h positioning accuracy. Compared with the traditional solution strategy, the precision of the method with respect to the north (N), east (E), upward (U), and zenith tropospheric delay (ZTD) significantly improved. The accuracy of U improved by more than 47%. Therefore, based on the high accuracy and reliability, information of ground stations can be fully utilized to significantly reduce the peak observation time and the operation costs of surveys in mountain regions.
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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 | 0.000 | 0.000 |
| 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 | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".