A VO `container' for astronomical optical/UV spectra
Bibliographic record
Abstract
FITS formats have provided a convenient means to access and interpret spectroscopic data for many years. However, each mission has been free to choose its FITS flavor and provide necessarily instrument specific keywords during this time. Over time, and with new missions developed with unique instrumental configurations, this has created a challenge for spectroscopic applications, which must be written in a complex fashion to recognize, read, and decipher the idiosyncrasies relating to each instrument. Moreover, some HST heritage instruments (GHRS, FOS) have stored different vectors in separate files, requiring their assembly before they can be used. With the advent of the VO and the first generation Simple Spectral Access Protocol (SSAP), it is possible to design a Spectral Container that addresses these issues by serving as a translation layer between the standardized VO protocols and the current FITS file formats. We have constructed SSAP services that point to a secondary data archive holding Spectral Container-packaged files for several MAST (Multi-Mission Archive at Space Telescope) missions. To date, these missions include those for which single-order observations are available: GHRS, FOS, EUVE, HUT, WUPPE, IUE, and STIS. In this poster we discuss the current status and examples of the Container using the Specview and VOSpec applications. We also discuss the need for second-generation VO protocols that will provide for multiple spectra (echelle multi-orders, time-series) within a single Container file.
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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 | 0.007 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.006 | 0.011 |
| Open science | 0.005 | 0.007 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.030 | 0.021 |
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".