Bibliographic record
Abstract
<strong class="journal-contentHeaderColor">Abstract.</strong> The Model for Simulating Aerosol Interactions and Chemistry (MOSAIC) aerosol thermodynamics and sectional framework has been implemented into the Canadian operational air quality model GEM-MACH. The original aerosol sub-model in GEM-MACH is based on the Canadian Aerosol Module (CAM), which uses a single-moment (mass) sectional scheme, and inorganic thermodynamics derived from the equilibrium ISORROPIA model without base metal cations. MOSAIC features non-equilibrium inorganic thermodynamics and a two-moment (mass and number) sectional scheme. For evaluation we conduct four one-year simulations with the same emissions and meteorology over the North America domain. A reference run (REF) with the Zhang et al. (2001) aerosol dry deposition scheme and a sensitivity run (EMR) with updated parameters from Emerson et al. (2020) is conducted for each aerosol model option. The results are compared to station observations and surface monthly-mean model-observation synthesis data. MOSAIC exhibits a shift in the accumulation mode mass and number distribution compared to CAM that results in more aerosol dry deposition in the REF run and a surface PM2.5 sulfate low bias of about 15 % relative to CAM. This bias is essentially removed in the MOSAIC EMR run resulting in a better fit to aggregated urban and rural stations compared to CAM over the North America domain. Comparison with the AERONET volume size distribution inversion product shows that MOSAIC gives a much higher level of agreement in terms of location of the accumulation mode peak diameter and separation of the accumulation and coarse modes. PM2.5 nitrate and ammonium for the MOSAIC EMR run shows overall better agreement with observation station data compared to both REF and EMR CAM runs at rural stations. At urban stations MOSAIC has a high bias for nitrate relative to CAM and observations during summer but it is reduced in the EMR run compared to the REF run. The high bias in ammonium seen with CAM for both REF and EMR runs relative to aggregated rural and urban station observations is reduced with MOSAIC by about 25 % between April and November.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.044 | 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 teacher head, 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".