The Canada‐France Deep Fields Survey. III. Photometric Redshift Distribution to <i>I</i> <sub>AB</sub> = 24
Bibliographic record
Abstract
We compute accurate redshift distributions to I AB = 24 and R AB = 24.5 using photometric redshifts estimated from six-band UBVRIZ photometry in the Canada-France Deep Fields Photometric Redshift Survey (CFDF-PRS). Our photometric redshift algorithm is calibrated using hundreds of CFRS spectroscopic redshifts in the same fields. The dispersion in redshift is σ/(1 + z ) ≲ 0.04 to the CFRS depth of I AB = 22.5, rising to σ/(1 + z ) ≲ 0.06 at our nominal magnitude and redshift limits of I AB = 24 and z ≤ 1.3, respectively. We describe a new method to compute N ( z ) that incorporates the full redshift likelihood functions in a Bayesian iterative analysis, and we demonstrate in extensive Monte Carlo simulations that it is superior to distributions calculated using simple maximum likelihood redshifts. The field-to-field differences in the redshift distributions, while not unexpected theoretically, are substantial even on 30' scales. We provide I AB and R AB redshift distributions, median redshifts, and parameterized fits of our results in various magnitude ranges, accounting for both random and systematic errors in the analysis.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.004 |
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".