Bibliographic record
Abstract
Abstract Star particles in a set of dense clusters are self-consistently evolved within an LCDM dark matter distribution with an n-body code. The clusters are started on nearly circular orbits in the more massive sub-halos. Each cluster develops a stellar tidal stream, initially within its original sub-halo. When a sub-halo merges into the main halo the early time stream is dispersed as a somewhat chaotic thick stream, roughly the width of the orbit of the cluster in the sub-halo. Once the cluster orbits freely in the main halo the star stream forms a thin stream again, usually resulting in a thin stream surrounded by a wider distribution of star particles lost at earlier times. To examine the role of the lower-mass dark matter sub-halos in the creation of density variations along the thin tidal star streams two realizations of the simulation are run with and without a normal cold dark matter sub-halo population below 4 × 108 . About 70(40)% of thin streams show density variations that are 2(5) times the star count noise level, irrespective of the presence or absence of low-mass sub-halos. A counts-in-cells analysis (related to the two-point correlation function and power spectrum) of the density along nearly 8000° of streams in the two well matched models finds that the full sub-halo population leads to slightly larger, but statistically significant, density fluctuations on scales of 2°–6°.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".