Searching for Low-redshift Faint Galaxies with MMT/Hectospec
Bibliographic record
Abstract
Abstract We present redshifts for 2753 low-redshift galaxies between 0.03 ≲ z spec ≲ 0.5 with 18 ≤ r ≤ 22 obtained with Hectospec at the Multi-Mirror Telescope. The observations targeted the XMM-LSS, ELAIS-N1 and DEEP2-3 fields, each of which covers ∼1 deg2. These fields are also part of the recently completed Canada–France–Hawaii Telescope Large Area U-band Deep Survey and ongoing Hyper Suprime-Cam deep fields surveys. The efficiency of our technique for selecting low-redshift galaxies is confirmed by the redshift distribution of our sources. In addition to redshifts, these high signal-to-noise ratio spectra are used to measure ages, metallicities, and nuclear activity levels. In combination with the photometric catalog in u, g, r, i, z, y down to 27 AB mag, we are able to study the galaxy population down to stellar masses of ∼108 M ⊙. This paper presents the observational strategy, the reduction procedure and properties of the galaxy sample. (The catalog can be accessed through the survey’s website at http://mips.as.arizona.edu/~cnaw/Faint_Low_z/ .)
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.004 |
| 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) | 0.004 | 0.002 |
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".