Galaxy Flow in the Canes Venatici I Cloud
Bibliographic record
Abstract
We present an analysis of Hubble Space Telescope/WFPC2 images of eighteen galaxies in the Canes Venatici I cloud. We derive their distances from the luminosity of the tip of the red giant branch stars with a typical accuracy of ~12 %. The resulting distances are 3.9 Mpc (UGC 6541), 4.9 Mpc (NGC 3738), 3.0 Mpc (NGC 3741), 4.5 Mpc (KK 109), >6.3 Mpc (NGC 4150), 4.2 Mpc (UGC 7298), 4.5 Mpc (NGC 4244), 4.6 Mpc (NGC 4395), 4.9 Mpc (UGC 7559), 4.2 Mpc (NGC 4449), 4.4 Mpc (UGC 7605), 4.6 Mpc (IC 3687), 4.7 Mpc (KK 166), 4.7 Mpc (NGC 4736), 4.2 Mpc (UGC 8308), 4.3 Mpc (UGC 8320), 4.6 Mpc (NGC 5204), and 3.2 Mpc (UGC 8833). The CVn I cloud has a mean radial velocity of 286 +- 9 km/s, a mean distance of 4.1 +- 0.2 Mpc, a radial velocity dispersion of 50 km/s, a mean projected radius of 760 kpc, and a total blue luminosity of 2.2 * 10^{10} L_{\\sun}. Assuming virial or closed orbital motions for the galaxies, we estimated their virial and their orbital mass-to-luminosity ratio to be 176 and 88 M_{\\sun}/L_{\\sun}, respectively. However, the CVn I cloud is characterized by a crossing time of 15 Gyr, and is thus far from a state of dynamical equilibrium. The large crossing time for the cloud, its low content of dSph galaxies ($ < 6$ %), and the almost ``primordial'' shape of its luminosity function show that the CVn I complex is in a transient dynamical state, driven rather by the free Hubble expansion than by galaxy interactions.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".