Details of Resonant Structures within a Nice Model Kuiper Belt: Predictions for High-perihelion TNO Detections
Bibliographic record
Abstract
Abstract We analyze a detailed Nice model simulation of Kuiper Belt emplacement from Brasser & Morbidelli, where Neptune undergoes a high-eccentricity phase and migrates outward. In this work, which follows from Pike et al., we specifically focus on the details of structures within Neptune’s mean-motion resonances and in the high-pericenter population of simulated trans-Neptunian objects (TNOs). We find several characteristics of these populations that should be observable in the distant solar system in future large-scale TNO surveys as a diagnostic of whether or not this mode of Neptune migration occurred in the early solar system. We find that the leading asymmetric libration islands of the n:1 resonances are generally much more populated than the trailing islands. We also find the nonresonant high-q population of TNOs should have higher inclinations than the low-q population due to the importance of Kozai cycling during their emplacement histories. Finally, high-q TNOs should be present in roughly equal numbers on either side of distant mean-motion resonances. These predictions contrast with predictions from other Kuiper Belt emplacement simulations and will be testable by upcoming surveys.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".