Preprint typeset using LATEX style emulateapj v. 5/14/03 THE CANADA–FRANCE DEEP FIELDS III: PHOTOMETRIC REDSHIFT DISTRIBUTION TO I AB ∼ 24
Bibliographic record
Abstract
We compute accurate redshift distributions to I AB = 24 and R AB = 24.5 using photometric redshifts estimated from 6–band 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. A direct application of our results is in weak lensing cosmic shear studies, in which accurate source and lens plane redshift distributions are required to extract cosmological parameters from the convergence power spectrum. We therefore provide our measured I AB and R AB redshift distributions, the run of median redshifts, and parametrized fits of our results in various magnitude ranges. We account for both random and systematic errors in our analysis, and discuss the effects of cosmic variance on our survey and
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.097 | 0.035 |
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".