Bibliographic record
Abstract
Abstract. By using a regional network of Global Positioning System (GPS) reference stations, it is possible to recover estimates of the slant wet delay (SWD) to all GPS satellites in view. SWD observations can then be used to model the vertical and horizontal structure of water vapor over a local area, using a tomographic approach. The University of Calgary currently operates a regional GPS real-time network of 14 sites in southern Alberta. This network provides an excellent opportunity to study severe weather conditions (e.g. thunderstorms, hail, and tornados) which develop in the foothills of the Rockies near Calgary. In this paper, a 4-D tomographic water vapor model is tested using the regional GPS network. A field campaign was conducted during July 2003 to derive an extensive set of truth data from radiosonde soundings. Accuracies of tomographic water vapor retrieval techniques are evaluated for 1) using only ground-based GPS input, and 2) using a ground-based GPS solution augmented with vertical wet refractivity profiles derived from radiosondes released within the GPS network. Zenith wet delays (ZWD) are computed for both cases, by integrating through the 4-D tomography predictions, and these values are compared with truth ZWD derived from independent radiosonde measurements. Results indicate that ZWD may be modeled with accuracies at the sub-centimeter level using a ground-based GPS network augmented with vertical profile information. This represents an improvement over the GPS-only approach.
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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 | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".