Enhancing VGOS Operations: Insights from R&D Sessions and Pathways Ahead
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it