Activities of the JWG 4.3.4 - Validation of VTEC models for high-precision and high resolution applications
Bibliographic record
Abstract
<!--!introduction!--> The global and regional ionospheric maps are often used for a wide range of applications in geosciences, in particular to support precise positioning, but also for geophysical and atmospheric studies. There are currently many analysis centers and research groups providing operational and test VTEC maps. However, IGS ACs and other groups use different mathematical models and estimation techniques resulting different resolutions, accuracies and time delays of their products. Therefore, there is a need to compare and validate existing VTEC models. In this presentation, we present the overview talk about the work within the last 4 years of the IAG Joint Working Group (JWG 4.3.4) on validation of VTEC models for high-precision and high resolution applications. Among others, we evaluated (1) the accuracy and consistency of the IAAC GIMs during high and low solar activity periods of the 24th solar cycle, (2) the accuracy the two most popular ionospheric mapping functions - SLM and MSLM, (3) deterministic and stochastic approaches to VTEC modelling, (4) GIMs performance in single point and precise point positioning GNSS applications, (5) the accuracy and consistency of GNSS-derived VTEC maps and empirical models, (6) GIMs performance using external data (JASON) and GNSS, (7) the accuracy of new global ionosphere models.
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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 | 0.034 | 0.032 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.007 |
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".