Bibliographic record
Abstract
Abstract Exoplanet demographic surveys have revealed that close-in (≲1 au) small planets orbiting stars in the Milky Way’s thick disk are ∼50% less abundant than those orbiting stars in the Galactic thin disk. One key difference between the two stellar populations is the time at which they emerged: thick-disk stars are the likely product of cosmic noon (redshift z ∼ 2), an era characterized by high star formation rate, massive and dense molecular clouds, and strong supersonic turbulence. Solving for the background radiation field in these early star-forming regions, we demonstrate that protoplanetary disks at cosmic noon experienced radiation fields up to ∼7 orders of magnitude more intense than in solar neighborhood conditions. Coupling the radiation field to a one-dimensional protoplanetary disk evolution model, we find that external UV photoevaporation destroys protoplanetary disks in just ∼0.2–0.5 Myr, limiting the timescale over which planets can assemble. Disk temperatures exceed the sublimation temperatures of common volatile species for ≳Myr timescales, predicting more spatial homogeneity in gas chemical composition. Our calculations imply that the deficit in planet occurrence around thick-disk stars should be even more pronounced for giant planets, particularly those at wide orbital separations, predicting a higher rocky-to-giant planet ratio in the Galactic thick disk versus thin 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 | 0.000 |
| 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) | 0.001 | 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".