Col-OSSOS: Probing Ice Line/Color Transitions within the Kuiper Belt’s Progenitor Populations
Bibliographic record
Abstract
Abstract Dynamically excited objects within the Kuiper Belt show a bimodal distribution in their surface colors, and these differing surface colors may be a tracer of where these objects formed. In this work, we explore radial color distributions in the primordial planetesimal disk and implications for the positions of ice line/color transitions within the Kuiper Belt’s progenitor populations. We combine a full dynamical model of the Kuiper Belt’s evolution due to Neptune’s migration with precise surface colors measured by the Colours of the Outer Solar System Origins Survey in order to examine the true color ratios within the Kuiper Belt and the ice lines within the primordial disk. We investigate the position of a dominant, surface color–changing ice line, with two possible surface color layouts within the initial disk: (1) inner neutral surfaces and outer red and (2) inner red surfaces and outer neutral. We performed simulations with a primordial disk that truncates at 30 au. By radially stepping the color transition out through 0.5 au intervals, we show that both disk configurations are consistent with the observed color fraction. For an inner neutral, outer red primordial disk, we find that the color transition can be at 28 − 3 + 2 au at a 95% confidence level. For an inner red, outer neutral primordial disk, the color transition can be at 27 − 3 + 3 au at a 95% confidence level.
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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 | 0.001 |
| 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 | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".