Bibliographic record
Abstract
Abstract The workshop on Data Management issues for Time-Domain Astronomy was conceived as a forward-looking discussion of the primary issues that need to be addressed for science in the time domain. The very broad diversity of the science areas presented in the main Symposium made it clear that most of the general issues for astronomy data management—for example, large data volumes, the need for timely processing and network performance—would be pertinent in the time domain. In addition, there might be other tight time constraints on data processing when the output was required to trigger rapid follow-up observations, while science based on very long time-baselines might require careful consideration of long-term data preservation and availability issues. But broadly speaking, data management challenges in the time domain are not at variance to any significant degree with those for astronomy or data-intensive research in general. The workshop framed and debated a number of questions: What is the biggest challenge faced by future projects? How do grid and cloud computing figure in data management plans? Is the Virtual Observatory important to future projects? How are the issues of data life cycle being addressed?
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.
How this classification was reachedexpand
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 | 0.002 | 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 | 0.003 | 0.003 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".