Canadian Aerosol Module: A size-segregated simulation of
Bibliographic record
Abstract
[1] A size-segregated multicomponent aerosol algorithm, the Canadian Aerosol Module (CAM), was developed for use with climate and air quality models. It includes major aerosol processes in the atmosphere: generation, hygroscopic growth, coagulation, nucleation, condensation, dry deposition/sedimentation, below-cloud scavenging, aerosol activation, a cloud module with explicit microphysical processes to treat aerosol-cloud interactions and chemical transformation of sulphur species in clear air and in clouds. The numerical solution was optimized to efficiently solve the complicated size-segregated multicomponent aerosol system and make it feasible to be included in global and regional models. An internal mixture is assumed for all types of aerosols except for soil dust and black carbon which are assumed to be externally mixed close to sources. To test the algorithm, emissions to the atmosphere of anthropogenic and natural aerosols are simulated for two aerosol types: sea salt and sulphate. A comparison was made of two numerical solutions of the aerosol algorithm: process splitting and ordinary differential equation (ODE) solver. It was found that the process-splitting method used for this model is within 15 % of the more accurate ODE solution for the total sulphate mass concentration and <1%
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".