Astrochemical Modeling of Propargyl Radical Chemistry in TMC-1
Bibliographic record
Abstract
Abstract Recent detections of aromatic species in dark molecular clouds suggest that formation pathways may be efficient at very low temperatures and pressures, yet current astrochemical models are unable to account for their derived abundances, which can often deviate from model predictions by several orders of magnitude. The propargyl radical, a highly abundant species in the dark molecular cloud TMC-1, is an important aromatic precursor in combustion flames and possibly interstellar environments. We performed astrochemical modeling of TMC-1 using the three-phase gas-grain code NAUTILUS and an updated chemical network, focused on refining the chemistry of the propargyl radical and related species. The abundance of the propargyl radical has been increased by half an order of magnitude compared to the previous GOTHAM network. This brings it closer in line with observations, but it remains underestimated by 2 orders of magnitude compared to its observed value. Predicted abundances for the chemically related C4H3N isomers within an order of magnitude of observed values corroborate the high efficiency of CN addition to closed-shell hydrocarbons under dark molecular cloud conditions. The results of our modeling provide insight into the chemical processes of the propargyl radical in dark molecular clouds and highlight the importance of resonance-stabilized radicals in polycyclic aromatic hydrocarbon formation.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".