Oort cloud asteroids: collisional evolution, the Nice Model, and the Grand Tack
Bibliographic record
Abstract
If the Solar system had a history of planet migration, the signature of that migration may be imprinted on the populations of asteroids and comets that were scattered in the planets’ wake. Here, we consider the dynamical and collisional evolution of the inner Solar system asteroids that join the Oort cloud. We compare the Oort cloud asteroid populations produced by migration scenarios based on the ‘Nice’ and ‘Grand Tack’ scenarios, as well as a null hypothesis where the planets have not migrated, to the detection of one such object, C/2014 S3 (PANSTARRS). Our simulations find that the discovery of C/2014 S3 (PANSTARRS) only has a |$\gt 1{{\ \rm per\ cent}}$| chance of occurring if the Oort cloud asteroids evolved on to Oort cloud orbits when the Solar system was |${\lesssim } 1\, \rm {Myr}$| old, as this early transfer to the Oort cloud is necessary to keep the amount of collisional evolution low. We argue that this only occurs when a giant (≳30m⊕) planet orbits at |$1\sim 2\, \rm {au}$|, and thus our results strongly favour a ‘Grand Tack’-like migration having occurred early in the Solar system’s history.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 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 | 0.001 |
| 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".