Infrastructure and the Virtual Observatory
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 modern data center is faced with architectural and software engineering challenges that grow along with the challenges facing observatories: massive data flow, distributed computing environments, and distributed teams collaborating on large and small projects. By using VO standards as key components of the infrastructure, projects can take advantage of a decade of intellectual investment by the IVOA community. By their nature, these standards are proven and tested designs that already exist. Adopting VO standards saves considerable design effort, allows projects to take advantage of open-source software and test suites to speed development, and enables the use of third party tools that understand the VO protocols. The evolving CADC architecture now makes heavy use of VO standards. We show examples of how these standards may be used directly, coupled with non-VO standards, or extended with custom capabilities to solve real problems and provide value to our users. In the end, we use VO services as major parts of the core infrastructure to reduce cost rather than as an extra layer with additional cost and we can deliver more general purpose and robust services to our user community.
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 | 0.000 |
| 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