Bibliographic record
Abstract
GD-1 is a long, thin, Milky Way star stream that has readily visible density variations along its length. We quantify the locations, sizes, and statistical significance of the density structure, i.e., gaps, using a set of scaled filters. The shapes of the filters are based on the gaps that develop in simulations of dark matter sub-halos crossing a star stream. The high Galactic latitude 8.4 kpc long segment of GD-1 that we examine has 8 ± 3 gaps of 99% significance or greater, with the error estimated on the basis of tests of the gap-filtering technique. The cumulative distribution of gaps more than three times the width of the stream is in good agreement with predictions for dark matter sub-halo encounters with cold star streams. The number of gaps narrower than three times the width of the GD-1 stream falls well below the cold stream prediction which is taken into account for the gap creation rate integrated over all sizes. Simple warm stream simulations scaled to GD-1 show that the falloff in gaps is expected for sub-halos below a mass of 10 6 M ☉ . The GD-1 gaps requires 100 sub-halos >10 6 M ☉ within 30 kpc, the apocenter of GD-1 orbit. These results are consistent with LCDM sub-halo predictions but further improvements in stream signal-to-noise and gap modeling will be welcome.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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) | 0.001 | 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".