Stochastic modelling of residual tropospheric delays / by Hassan Elobeid Ibrahim
Bibliographic record
Abstract
Real-time and near real-time precise point positioning (PPP) requires shorter solution convergence time. Residual tropospheric delay, which exists as a result of the limitations of current tropospheric correction models, is a limiting factor for fast PPP convergence. To overcome the limitations of existing tropospheric models, we proposed a new approach. In this approach, the bulk of the tropospheric delay is accounted for using an empirical model, while the residual component is accounted for stochastically. The analysis of many daily tropospheric residuals data series for stations spanning North America shows that the residual component can be accounted for using an exponential cosine model. A random walk (RW) model was also developed and used along with the NOAA tropospheric corrections with Vienna Mapping Function 1. It is shown that the RW improved the accuracy of station coordinates within the PPP convergence time by a few centimetres.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".