Improved Tropospheric Delay Estimation for Long Baseline, Carrier-Phase Differential GPS Positioning in a Coastal Environment
Bibliographic record
Abstract
Long baseline, carrier-phase differential GPS positioning in a coastal environment poses unique challenges. It is well known that differential GPS positioning results degrade as baseline length increases due to several sources of error, including the error introduced by differential troposphere. The effect of the troposphere on GPS has been extensively discussed by numerous researchers, either by comparing the resolution of tropospheric prediction models or by assessing the tropospheric delay directly on GPS measurements and results. In order to improve the estimation of tropospheric delay in the coastal environment, a project has been undertaken by the University of New Brunswick (UNB) and the University of Southern Mississippi (USM). The project includes extensive GPS and meteorological data collection in the Bay of Fundy in Canada. The goal of the research is to examine methods for improving tropospheric delay estimation by employing various sources of data. This includes the use of surface meteorological parameters and Numerical Weather Prediction (NWP) model data. For this research, NWP data are accessed from the Canadian Meteorological Centre’s (CMC) regional model and from the National Oceanic and Atmospheric Administration’s (NOAA) tropospheric delay product. Tropospheric delays modelled from the NWP model data are compared with those from global prediction models. Results in this paper will demonstrate the effect of using surface meteorological data and NWP model data to estimate the tropospheric delay in a coastal environment.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".