Development of a simple unified volatility-based scheme (SUVS) for secondary organic aerosol formation using genetic algorithms
Bibliographic record
Abstract
Abstract. A new method is proposed to simplify complex atmospheric chemistry reaction schemes, while preserving SOA formation properties, using genetic algorithms. The method is first applied in this study to the gas-phase α-pinene oxidation scheme. The simple unified volatility-based scheme (SUVS) reflects the multi-generation evolution of chemical species from a near-explicit master chemical mechanism (MCM) and, at the same time, uses the volatility-basis set speciation for condensable products. The SUVS also unifies reactions between SOA precursors with different oxidants under different atmospheric conditions. A total of 412 unknown parameters (product yields of parameterized products, reaction rates, etc.) from the SUVS are estimated by using genetic algorithms operating on the detailed mechanism. The number of organic species was reduced from 310 in the detailed mechanism to 31 in the SUVS. Output species profiles, obtained from the original subset of the MCM reaction scheme for α-pinene oxidation, are reproduced with maximum fractional error at 0.10 for scenarios under a wide range of ambient HC/NOx conditions. Ultimately, the same SUVS with updated parameters could be used to describe the SOA formation from different precursors.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| 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".