Bibliographic record
Abstract
This paper presents the results of collisional evolution calculations for the Kuiper Belt starting from an initial size distribution similar to that produced by accretion simulations of that region—a steep power-law large object size distribution that breaks to a shallower slope at r ∼ 1–2 km, with collisional equilibrium achieved for objects r ≲ 0.5 km. We find that the break from the steep large object power law causes a divot, or depletion of objects at r ∼ 10–20 km, which, in turn, greatly reduces the disruption rate of objects with r ≳ 25–50 km, preserving the steep power-law behavior for objects at this size. Our calculations demonstrate that the roll-over observed in the Kuiper Belt size distribution is naturally explained as an edge of a divot in the size distribution; the radius at which the size distribution transitions away from the power law, and the shape of the divot from our simulations are consistent with the size of the observed roll-over, and size distribution for smaller bodies. Both the kink radius and the radius of the divot center depend on the strength scaling law in the gravity regime for Kuiper Belt objects. These simulations suggest that the sky density of r ∼ 1 km objects is ∼10 6 –10 7 objects per square degree. A detection of the divot in the size distribution would provide a measure of the strength of large Kuiper Belt objects, and constrain the shape of the size distribution at the end of accretion in the Kuiper Belt.
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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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".