UNIVERSITY OF CALGARY Data Assimilation for 4-D Wet Refractivity Modelling in a Regional GPS Network
Bibliographic record
Abstract
One source of error for GPS signals received on the Earth is due to the delay caused by propagation through the troposphere. If a receiver’s coordinates are known and surface pressure measurements are available, the positioning problem can be inverted such that the wet delay in the signal can be used to derive water vapour content in the atmosphere through a tomographic, 4-D model. Local radiosonde observations, monthly-averaged climate data and GPS occultation-derived wet refractivity measurements were assimilated into a tomography model which originally used ground-based GPS data over southern Alberta. Improvements were made to the estimation of vertical profiles of water vapour, and improvements in the integrated domain were on the order of ~0.5 cm for the assimilation of radiosonde data. The best results were obtained by assimilating radiosonde observations. Occultation measurement assimilation resulted in improvements in the integrated domain of up to 0.5 cm. ii ACKNOWLEDGMENTS
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.003 |
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".