The Canada-France deep fields survey
Bibliographic record
Abstract
Using the University of Hawaii's 8K mosaic camera (UH8K), we have measured the angular correlation function for 100 000 galaxies distributed over four widely separated fields totalling ~ and reaching a limiting magnitude of . This unique combination of areal coverage and depth allows us to investigate the dependence of at , , on sample median magnitude in the range . Furthermore, our rigorous control of systematic photometric and astrometric errors means that fainter than we measure on scales of several arc-minutes to an accuracy of . Our results show that decreases monotonically to . At bright magnitudes, is consistent with a power-law of slope for but at fainter magnitudes we detect a slope flattening with . At the level, our observations are still consistent with . We also find a clear dependence of on observed colour. In the magnitude ranges and we find galaxies with (the reddest bin we consider) have 's which are ~ higher than the full field population. On the basis of their similar colours and clustering properties, we tentatively identify these objects as a superset of the "extremely red objects" found through optical-infrared selection. We demonstrate that our model predictions for the redshift distribution for the faint galaxy population are in good agreement with current spectroscopic observations. Using these predictions, we find that for low-Ω cosmologies and assuming a local galaxy correlation length Mpc, in the range , the growth of galaxy clustering (parameterised by ϵ), is . However, at , our observations are consistent with . Models with cannot simultaneously match both bright and faint measurements of . We show how this result is a natural consequence of the "bias-free" nature of the "ϵ" formalism and is consistent with the field galaxy population in the range being dominated by galaxies of low intrinsic luminosity.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.018 | 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".