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 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. 
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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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