Numerical solution for atmospheric multiphase models: Testing the validity of equilibrium assumptions
Bibliographic record
Abstract
Fast equilibria, such as ionic dissociations within cloud drops and the establishment of Henry's law at the air‐liquid interface between dissolved chemical species and the corresponding vapour pressure, may occur under special conditions. The main advantage of making equilibrium assumptions is to allow the calculation of some chemical species concentrations using simple equilibrium relations at any time without the need for time integration. The other advantage of these assumptions is the elimination of stiffness and thus the saving of CPU time which is important for three dimensional models. An existing algorithm is used to scale the system of Ordinary Differential Equations (ODE) describing a chemical box‐model and the computation of the exact lumping of species. Such a scaling analysis allows us to evaluate whether an equilibrium assumption can be made without loosing accuracy. Furthermore, the algorithm determines the exact lumping of species to be introduced in order to keep the model valid while assuming equilibrium. The resulting reduced model consists of a set of ODEs and a set of algebraic relations describing the equilibrium state. The results show that according to the magnitudes of the characteristic timescales, hierarchical underlying reduced models may exist where the computed lumpings of species are mainly related to the fast ionizations and Henry's law, but also some mixed lumpings. The study shows that equilibrium may or may not be valid for individual mass transfer and ionization/dissociation reactions under different conditions. The chemical mechanism examined here is an extension of the Regional Acid Deposition Mechanism (RADM2) including sulfur and transition metal chemistry.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 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".