Col-OSSOS: The Distribution of Surface Classes in Neptune's Resonances
Bibliographic record
Abstract
Abstract The distribution of surface classes of resonant trans-Neptunian objects (TNOs) provides constraints on the protoplanetesimal disk and giant planet migration. To better understand the surfaces of TNOs, the Colours of the Outer Solar System Origins Survey acquired multiband photometry of 102 TNOs and found that the surfaces of TNOs can be well described by two surface classifications: BrightIR and FaintIR. These classifications both include optically red members and are differentiated predominantly based on whether their near-infrared spectral slope is similar to their optical spectral slope. The vast majority of cold classical TNOs, with dynamically quiescent orbits, have the FaintIR surface classification, and we infer that TNOs in other dynamical classifications with FaintIR surfaces share a common origin with the cold classical TNOs. Comparison between the resonant populations and the possible parent populations of cold classical and dynamically excited TNOs reveal that the 3:2 has minimal contributions from the FaintIR class, which could be explained by the ν 8 secular resonance clearing the region near the 3:2 before any sweeping capture occurred. Conversely, the fraction of FaintIR objects in the 4:3 resonance, 2:1 resonance, and the resonances within the cold classical belt suggest that the FaintIR surface formed in the protoplanetary disk between ≳34.6 and ≲47 au, though the outer bound depends on the degree of resonance sweeping during migration. The presence and absence of the FaintIR surfaces in Neptune’s resonances provides critical constraints for the history of Neptune’s migration, the evolution of the ν 8, and the surface class distribution in the initial planetesimal disk.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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".