The Hubble Legacy Archive NICMOS grism data
Bibliographic record
Abstract
The Hubble Legacy Archive (HLA) aims to create calibrated science data from the Hubble Space Telescope archive and make them accessible via user-friendly and Virtual Observatory (VO) compatible interfaces. It is a collaboration between the Space Telescope Science Institute (STScI), the Canadian Astronomy Data Centre (CADC) and the Space Telescope – European Coordinating Facility (ST-ECF). Data produced by the Hubble Space Telescope (HST) instruments with slitless spectroscopy modes are among the most difficult to extract and exploit. As part of the HLA project, the ST-ECF aims to provide calibrated spectra for objects observed with these HST slitless modes. In this paper, we present the HLA NICMOS G141 grism spectra. We describe in detail the calibration, data reduction and spectrum extraction methods used to produce the extracted spectra. The quality of the extracted spectra and associated direct images is demonstrated through comparison with near-IR imaging catalogues and existing near-IR spectroscopy. The output data products and their associated metadata are publicly available (http://hla.stecf.org/) through a web form, as well as a VO-compatible interface that enables flexible querying of the archive of the 2470 NICMOS G141 spectra. In total, spectra of 1923 unique targets are included.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.008 | 0.006 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.022 | 0.020 |
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".