1 CFHTLS-T0007 Executive Summary
Bibliographic record
Abstract
This document describes T0007, the 7th and final release of the Canada-France-Hawaii Telescope Legacy Survey CFHTLS1, produced by Terapix2 based on a data set collected with MegaCam3 on the CFHT. CFHTLS-T0007 is a deep sub-arcsecond (0.8′′) wide-field (157 deg2 total) optical survey (u∗, g, r, i, z bands) providing a high quality and homogeneous data set precisely calibrated photometrically (1.0%) and astrometrically (0.028′′). This final release is directly public, open to the worldwide community. CFHTLS-T0007 has two components: 1) the “CFHTLS Deep”, four independent 1 deg2 MegaCam ultra deep pointings, reaching a 80 % completeness limit in AB of u∗=26.3, g=26.0, r=25.6, i=25.4, z=25.0 for point sources, and 2) the “CFHTLS Wide ” made of 171 MegaCam deep pointings which, due to overlaps between adjacent fields consists of a total of ∼ 155 deg2 in four independent contiguous patches, reaching a 80 % completeness limit in AB of u∗=25.2, g=25.5, r=25.0, i=24.8, z=23.9 for point sources. The sky location of these fields is shown in Figure 1. This final release of the CFHTLS greatly benefits from vastly improved flat-fielding and photometric calibration techniques developed by the Supernova Legacy Survey (SNLS) team in collaboration with the CFHT. These new recipes significantly improve the precision of our photometric calibration compared to previous releases. T0007 is derived from a parent sample comprising all validated images taken during the survey between
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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 | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.614 | 0.627 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".