The WIRCam Deep Survey I: Counts, colours and mass-functions derived from near-infrared imaging in the CFHTLS deep fields
Bibliographic record
Abstract
We present a new near-infrared imaging survey in the four CFHTLS deep fields: the WIRCam Deep Survey (WIRDS). WIRDS comprises extremely deep, high quality (FWHM ~0.6") J, H and K imaging covering a total effective area of 2.1 deg^2 and reaching AB 50% completeness limits of ~24.5. We combine our images with the CFHTLS to create a unique eight-band ugrizJHK photometric catalogues in the CFHTLS deep fields; these four separate fields allow us to make a robust estimate of the effect of cosmic variance for all our measurements. We use these catalogues to estimate precise photometric redshifts, galaxy types and stellar masses for a unique sample of ~1.8 million galaxies. Our JHK number counts are consistent with previous studies. We apply the BzK selection to our gzK filter set and find that the star forming BzK selection successfully selects 76% of star-forming galaxies in the redshift range 1.4
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".