Bibliographic record
Abstract
ABSTRACT The GD-1 star stream is currently the best available for identifying density fluctuations, “gaps,” along its length as a test of the LCDM prediction of large numbers of dark matter sub-halos orbiting in the halo. Density variations of some form are present, since the variance of the density along the stream is three times that expected from the empirically estimated variation in the filtered mean star counts. The density variations are characterized with filters that approximate the shape of sub-halo, gravitationally induced stream gaps. The filters locate gaps and measure their amplitude, leading to a measurement of the distribution of gap widths. To gain an understanding of the factors influencing the gap width distribution, a suite of collisionless n-body simulations for a GD-1-like orbit in a Milky-Way-like potential provides a dynamically realistic statistical prediction of the gap distribution. The simulations show that every location in the stream has been disturbed to some degree by a sub-halo. The small gaps found via the filtering are largely noise. Larger gaps, those longer than 1 kpc, or 10° for GD-1, are the source of the excess variance. The suite of stream simulations shows that sub-halos at the predicted inner halo abundance or possibly somewhat higher can produce the required large-scale density variations.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".