Bibliographic record
Abstract
Abstract A Milky Way–like halo is simulated with tidally limited star clusters inserted in the dark matter halos present at high redshift. An n-body code augmented with velocity relaxation in the star clusters evolves the system. The stripped stars and remnant clusters that began in the lower mass subhalos have a distribution somewhat more extended than the dark matter halo, with a mean galactic radius of about 60 kpc inside 150 kpc, whereas the distribution of all stripped stars and clusters is more centrally concentrated than the dark matter. The clusters from low mass subhalos, those with a peak circular velocity of 12–18 <?CDATA $\,\mathrm{km}\,{{\rm{s}}}^{-1}$?> , also produce most of the population’s thin stellar streams. Together these suggest a search strategy for extremely metal-poor cluster stars and their remnants. The dependence of the stellar population distribution on subhalo mass is not seen in simulations that start clusters at lower redshift. The half mass radii of the clusters are set by the tidal fields of the initial subhalo, “dwarf galaxy” location, which causes the average half mass–radius to decrease with increasing redshift of formation. Starting clusters at greater than redshift 4 leads to cluster half mass radii approximately as seen in the Milky Way.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".