Radial Gradients in Dust-to-gas Ratio Lead to Preferred Region for Giant Planet Formation
Bibliographic record
Abstract
Abstract The Rosseland mean opacity of dust in protoplanetary disks is often calculated assuming the interstellar medium (ISM) size distribution and a constant dust-to-gas ratio. However, the dust size distribution and dust-to-gas ratio in protoplanetary disks are distinct from those of the ISM. Here we use simple dust evolution models that incorporate grain growth and transport to calculate the time evolution of the mean opacity of dust grains as a function of distance from the star. Dust dynamics and size distribution are sensitive to the assumed value of the turbulence strength α t and the velocity at which grains fragment v frag. For moderate-to-low turbulence strengths of α t ≲ 10−3 and substantial differences in v frag for icy and ice-free grains, we find a spatially nonuniform dust-to-gas ratio and grain size distribution that deviate significantly from the ISM values, in agreement with previous studies. The effect of a nonuniform dust-to-gas ratio on the Rosseland mean opacity dominates over that of the size distribution. This spatially varying—that is, non-monotonic—dust-to-gas ratio creates a region in the protoplanetary disk that is optimal for producing hydrogen-rich planets, potentially explaining the apparent peak in the gas-giant planet occurrence rate at intermediate distances. The enhanced dust-to-gas ratio within the ice line also suppresses gas accretion rates onto sub-Neptune cores, thus stifling their tendency to undergo runaway gas accretion within disk lifetimes. Finally, our work corroborates the idea that low-mass cores with large primordial gaseous envelopes (“super-puffs”) originate beyond the ice line.
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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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".