SIGNALS: I. Survey description
Bibliographic record
Abstract
ABSTRACT SIGNALS, the Star formation, Ionized Gas, and Nebular Abundances Legacy Survey, is a large observing programme designed to investigate massive star formation and H ii regions in a sample of local extended galaxies. The programme will use the imaging Fourier transform spectrograph SITELLE at the Canada–France–Hawaii Telescope. Over 355 h (54.7 nights) have been allocated beginning in fall 2018 for eight consecutive semesters. Once completed, SIGNALS will provide a statistically reliable laboratory to investigate massive star formation, including over 50 000 resolved H ii regions: the largest, most complete, and homogeneous data base of spectroscopically and spatially resolved extragalactic H ii regions ever assembled. For each field observed, three datacubes covering the spectral bands of the filters SN1 (363–386 nm), SN2 (482–513 nm), and SN3 (647–685 nm) are gathered. The spectral resolution selected for each spectral band is 1000, 1000, and 5000, respectively. As defined, the project sample will facilitate the study of small-scale nebular physics and many other phenomena linked to star formation at a mean spatial resolution of ∼20 pc. This survey also has considerable legacy value for additional topics, including planetary nebulae, diffuse ionized gas, and supernova remnants. The purpose of this paper is to present a general outlook of the survey, notably the observing strategy, galaxy sample, and science requirements.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.009 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.167 | 0.180 |
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".