Bibliographic record
Abstract
A primary goal of the Virtual Observatory (VO) is the integration of data from many instruments and wavelengths and creating source and objects lists to facilitate pan-chromatic science. Astrometry is essential to define the positional coincidences (either static position or motion in the solar system, galaxy, or other system) that mark multiple observations of flux as likely to be originating from the same physical source. Support for VO is based on the community experience with data archives like that of the Hubble Space Telescope (HST) and the extremely high science value of large, homogeneous surveys, for example the Sloan Digital Sky Survey. These experiences have clarified the fundamental role that advanced information technology plays in astronomy research. Groups around the world have drawn lessons from these experiences that have changed the way that astronomical data is collected and managed. We discuss the Canada-France-Hawaii Telescope Legacy Survey and the WFPC2 Association Stacks project. These projects demonstrate some of the ways in which data management has changed and we point out the essential role that astrometry plays in migrating the data output of these projects into VO-like systems and user interfaces. High quality astrometry has been under-valued in the past because data management issues focussed on the needs of a single Principal Investigator. It is now clear that data have little value for large-scale VO projects if they lack reliable astrometry.
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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 | 0.005 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.002 | 0.009 |
| Scholarly communication | 0.009 | 0.010 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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 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".