Spectrophotometric properties of galaxies at intermediate redshifts(<i>z</i> ~ 0.2–1.0)
Bibliographic record
Abstract
We present the spectrophotometric properties of a sample of 141 emission-line galaxies at redshifts in the range with a peak around . The analysis is based on medium resolution (), optical spectra obtained at VLT and Keck. The targets are mostly “Canada-France Redshift Survey” emission-line galaxies, with the addition of field galaxies randomly selected behind lensing clusters. We complement this sample with galaxy spectra from the “Gemini Deep Deep Survey” public data release. We have computed absolute magnitudes of the galaxies and measured the line fluxes and equivalent widths of the main emission/absorption lines. The last two have been measured after careful subtraction of the fitted stellar continuum using the platefit software originally developed for the SDSS and adapted to our data. We present a careful comparison of this software with the results of manual measurements. The pipeline has also been tested on lower resolution spectra, typical of the “VIMOS/VLT Deep Survey” (), by resampling our medium resolution spectra. We show that we can successfully deblend the most important strong emission lines. These data are primarily used to perform a spectral classification of the galaxies in order to distinguish star-forming galaxies from AGNs. Among the initial sample of 141 emission-line galaxies, we find 7 Seyfert 2 (narrow-line AGN), 115 star-forming galaxies and 16 “candidate” star-forming galaxies. Scientific analysis of these data, in terms of chemical abundances, stellar populations, etc., will be presented in subsequent papers of this serie.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".