Mechanism reduction for the formation of secondary organic aerosol for integration into a 3-dimensional regional air quality model: <i>α</i> -pinene oxidation system
Bibliographic record
Abstract
Abstract. A detailed α-pinene oxidation mechanism was reduced systematically through the successive application of five mechanism reduction techniques. The resulting reduced mechanism preserves the ozone- and organic aerosol-forming properties of the original mechanism, while using less species. The methodologies employed included a directed relation graph method with error propagation (DRGEP, which removed a large number of redundant species and reactions), principal component analysis of the rate sensitivity matrix (PCA, used to remove unnecessary reactions), the quasi-steady-state approximation (QSSA, used to remove some QSS species), an iterative screening method (ISSA, which removes redundant species and reactions simultaneously), and a new lumping approach dependent on the hydrocarbon to NOx ratio (which reduced the number of species in mechanism subsets for specific hydrocarbon to NOx ranges). This multistage methodology results in a reduction ratio of 2.5 for the number of both species and reactions compared with the full mechanism. The simplified mechanism reproduces the important gas and aerosol phase species (the latter are examined in detail by individual condensing species as well as in classes according to four functional groups: PANs, nitrates, organic peroxides, and organic acids). The total SOA mass is also well represented in the condensed mechanism, to within 16% of the detailed mechanism under a wide range of conditions. The methodology described here is general, and may be used in general mechanism reduction problems.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".