Near real-time water vapor distribution surface rendering using Ordinary KrigingThis article is one of a series of papers published in this Special Issue on the theme<i> GEODESY</i>.
Bibliographic record
Abstract
Based on the near real-time Global Positioning System (GPS) precise pointing positioning (PPP)-inferred water vapor system recently developed at the University of Calgary, Calgary, Alberta, an Ordinary Kriging procedure has been developed to predict the local and regional precipitable water vapor (PWV) and describe its distribution over Canada using limited available data. The Ordinary Kriging procedure includes five steps: (1) quantifying PWV spatial structure by calculating an experimental semivariogram; (2) fitting semivariogram models (spherical, exponential, and Gaussian) with nonlinear, weighted least squares; (3) determining the best-fitted model with cross-validation analysis; (4) estimating whole PWV maps by Ordinary Kriging interpolation; and (5) outputting kriging standard error maps. The 24 h variogram analysis shows that the correctly calculated experimental semivariogram is essential to the accuracy of the kriged maps, which depends on the configuration of the sites, lag step, and lag tolerance. The optimal lag step and lag tolerance for the current Canadian GPS network configuration are 5° and 2.5°, respectively. Among the three semivariogram models, the spherical model fails in its performance most of the time, and the best hourly fitted semivariogram model is either the exponential (90%) or the Gaussian (10%) model. The Gaussian-model-based Ordinary Kriging process produces more detailed maps. The surface maps of the kriging standard errors indicate that the area between longitudes –125° and –60° and latitudes 44° and 54° has higher accuracy due to higher availability of data.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".