Rethinking operational VGOS observations
Bibliographic record
Abstract
The VLBI Global Observing System (VGOS) was created to meet the ambitious requirements set by the Global Geodetic Observing System (GGOS). Its primary objective is achieving millimeter-level precision while maintaining continuous 24/7 observations. Currently, both aims remain unfulfilled. Simultaneously, new requirements, such as the development of a dedicated VGOS Celestial Reference Frame (CRF), have emerged. Thus, a reevaluation of our current VGOS observational framework is necessary to reach the VGOS goals.This study addresses three pivotal challenges within VGOS: attaining millimeter precision, providing observations for a CRF, and achieving uninterrupted 24/7 observations. Each of these topics demand a readjustment of our current observation scheduling methodology.Based on insight from VGOS R&D sessions, this work discusses potential approaches to meet the requisite precision through shorter, signal-to-noise-driven observations. Additionally, it explores the combination of this methodology with source-based scheduling to facilitate the creation of essential observations for establishing a dedicated VGOS CRF. Finally, it addresses the issue of reaching 24/7 observations, currently limited by data transfer and correlation capacities. To overcome this, a potential solution involves a significant reduction in the recorded data volume per session by temporarily thinning out the schedule. Thus, it comes with a trade-off in precision. This concept might be seen as a paradigm shift in VLBI observations, traditionally striving for the highest precision possible, which we believe is worth being discussed. Based on observation statistics and Monte-Carlo simulations, we will elaborate on the expected impact of this 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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.002 |
| 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".