A POSSIBLE DIVOT IN THE SIZE DISTRIBUTION OF THE KUIPER BELT'S SCATTERING OBJECTS
Bibliographic record
Abstract
Via joint analysis of a calibrated telescopic survey, which found scattering Kuiper Belt objects, and models of their expected orbital distribution, we explore the scattering-object (SO) size distribution. Although for D > 100 km the number of objects quickly rise as diameters decrease, we find a relative lack of smaller objects, ruling out a single power law at greater than 99% confidence. After studying traditional "knees" in the size distribution, we explore other formulations and find that, surprisingly, our analysis is consistent with a very sudden decrease (a divot) in the number distribution as diameters decrease below 100 km, which then rises again as a power law. Motivated by other dynamically hot populations and the Centaurs, we argue for a divot size distribution where the number of smaller objects rises again as expected via collisional equilibrium. Extrapolation yields enough kilometer-scale SOs to supply the nearby Jupiter-family comets. Our interpretation is that this divot feature is a preserved relic of the size distribution made by planetesimal formation, now "frozen in" to portions of the Kuiper Belt sharing a "hot" orbital inclination distribution, explaining several puzzles in Kuiper Belt science. Additionally, we show that to match today's SO inclination distribution, the supply source that was scattered outward must have already been vertically heated to the of order 10°.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.001 |
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".