period was the Spitzer First Look Survey. The survey provided
Bibliographic record
Abstract
We present deep u ∗-, and g-band images taken with the MegaCam on the 3.6 m Canada-France-Hawaii Telescope (CFHT) to support the extragalactic component of the Spitzer First Look Survey (hereafter, FLS). In this paper we outline the observations, present source catalogs and characterize the completeness, reliability, astrometric accuracy and number counts of this dataset. In the central 1 deg 2 region of the FLS, we reach depths of g ∼26.5 mag, and u ∗ ∼26.2 mag (AB magnitude, 5σ detection over a 3 ′ ′ aperture) with ∼4 hours of exposure time for each filter. For the entire FLS region (∼5 deg 2 coverage), we obtained u ∗-band images to the shallower depth of u ∗ =25.0–25.4 mag (5σ, 3 ′ ′ aperture). The average seeing of the observations is 0. ′ ′ 85 for the central field, and ∼1. ′ ′ 00 for the other fields. Astrometric calibration of the fields yields an absolute astrometric accuracy of 0. ′ ′ 15 when matched with the SDSS point sources between 18< g <22. Source catalogs have been created using SExtractor. The catalogs are 50 % complete and greater than 99.3 % reliable down to g ≃ 26.5 mag and u ∗ ≃ 26.2 mag for the central 1 deg 2 field. In the shallower u ∗-band images, the catalogs are 50% complete and 98.2 % reliable down to 24.8–25.4 mag. These images and source catalogs will serve as a useful resource for studying the galaxy evolution using the FLS data. Subject headings: catalogs—surveys—galaxies:photometry 1.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.018 | 0.009 |
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".