Enhancing VGOS Operations: Insights from R&D Sessions and Pathways Ahead
Bibliographic record
Abstract
The inception of the VGOS R&D program in 2021 marked a pivotal milestone in the evolution of VGOS. This work serves as a catalyst for an interactive discussion, providing a platform to discuss lessons learned from these sessions while charting pathways for future VGOS observations and operational integration.Our focus revolves around the outcomes gleaned from the six VGOS R&D sessions conducted in 2022. These sessions aimed at optimizing the number and distribution of observations and scans, resulting in a significant augmentation, with observations and scans more than doubling compared to conventional VGOS sessions while simultaneously reducing the number of recorded bits. Noteworthy enhancements were observed in Earth orientation parameter estimates, showcasing improved alignment with IERS solutions and SX observations, coupled with bolstered baseline length repeatability and reduced formal errors.Furthermore, our exploration delves into the pioneering two sessions of 2023, trialing source-based VLBI scheduling. This initiative aimed at expanding the VGOS source list through the integration of new ICRF3 sources while amplifying imaging capabilities.Our findings underscore the pivotal advantages of equitably distributing observations among sources, presenting compelling benefits for the VGOS framework.This poster serves as an invitation to engage in a discussion that encapsulates the successes and insights derived from the VGOS R&D sessions. It aims to stimulate discourse on strategies for seamless integration into operational VGOS sessions, fostering a collaborative environment to utilize VGOS capabilities for future scientific endeavors.
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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 | 0.029 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.013 | 0.010 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.008 | 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".