The Effect of Carbon Grain Destruction on the Chemical Structure of Protoplanetary Disks
Bibliographic record
Abstract
Abstract The bulk composition of Earth is dramatically carbon-poor compared to that of the interstellar medium, and this phenomenon extends to the asteroid belt. To interpret this carbon deficit problem, the carbonaceous component in grains must have been converted into the gas phase in the inner regions of protoplanetary disks (PPDs) prior to planetary formation. We examine the effect of carbon grain destruction on the chemical structure of disks by calculating the molecular abundances and distributions using a comprehensive chemical reaction network. When carbon grains are destroyed and the elemental abundance of the gas becomes carbon-rich, the abundances of carbon-bearing molecules, such as HCN and carbon-chain molecules, increase dramatically near the midplane, while oxygen-bearing molecules, such as and , are depleted. We compare the results of these model calculations with the solid carbon-to-silicon fraction in the solar system. Although we find a carbon depletion gradient, there are some quantitative discrepancies: the model shows a higher value at the position of the asteroid belt and a lower value at the location of Earth. In addition, using the obtained molecular abundance distributions, coupled with line radiative transfer calculations, we make predictions for ALMA to potentially observe the effect of carbon grain destruction in nearby PPDs. The results indicate that HCN, , and c- may be good tracers.
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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".