<u>S</u>wiss-<u>C</u>anadian <u>r</u>esearch on <u>a</u>erosol <u>m</u>odelling (SCRAM)
Bibliographic record
Abstract
The SCRAM Project (Swiss-Canadian Research on Aerosol Modelling) is a collaboration between two research groups to assess the importance of particulate matter in the regional atmospheres in Switzerland and southern Ontario. In this study, two state-of-the-art chemical transport models are applied to simulate meteorology and tropospheric chemistry with a focus on the formation and transport of particulate matter (PM) in Switzerland for a period in winter 2006 and in Southern Ontario in summer 2001. The application of two models with different aerosol modules (CAMx and CMAQ-MADRID) on the same domains will provide unique information about the strengths and/or weaknesses of models. In this paper, the progress of the SCRAM project is reported. The preparation of emission inventory and the modelling of the meteorological parameters for the period between 1 January and 10 February 2006 over the Swiss domains have been completed and simulations with the air quality model CAMx have already started. The results of the first 14 days of the Swiss simulations look promising. The measurements with the aerosol mass spectrometer (AMS) and model predictions show that the particle mass concentrations are dominated by organic and nitrate aerosols at urban and motorway sites. The modelled concentrations of inorganic aerosols agree better with the measurements while organics are underestimated. The CAMx model predicts that the organic aerosols are mostly primary and the secondary organic aerosols (SOA) formed from biogenic emissions dominate the total SOA concentrations at the urban site. The detailed comparison of the model results with measurements will be reported later as soon as the modelling of whole period is completed. The meteorological modelling and preparation of emission inventory have also been completed over the Southern Ontario domains and simulations with CMAQ-MADRID model are in progress.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.003 |
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; both teacher heads agree on what is shown here.
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".