Seasonal comparisons of GEOS-Chem-TOMAS (GCT) simulations with AERONET-inversion retrievals over sites in the North American and European Arctic
Bibliographic record
Abstract
GEOS-Chem TOMAS (GCT) simulations of AERONET-inversion products during 2015 were compared with AERONET-inversion products from the multi-year climatology of Aboel-Fetouh et al. (2020) (AeF) and for year 2015 acquired over 5 stations in the North American and European Arctic. The GCT simulations of particle size distributions (PSD) did not capture a spring to summer radius increase of the fine mode (FM) peak observed by AeF but did capture AeF’s springtime coarse mode (CM) peak (small-sized CM peak with a radius ~ 1.3 µm) and a weak late summer / fall increase in the amplitude of that peak. The lack of a spring to summer FM radius increase was likely due to the large GCT cell size (4° x 5°) and associated difficulties in the modelling of coagulation-induced smoke particle size. Conversely, the GCT simulation of the small-sized CM peak indicated a successful capture of the springtime influx of Asian dust. The fall increase of that GCT peak was associated with an increase of a larger (4 -7 µm) PSD mode that AeF suggested was due to local dust. GCT captured the seasonal (climatological-scale) FM AOD trend, the decreasing CM AOD trend, and the increasing trend of the FM fraction. The GCT CM AOD also showed a fall increase that was coherent with the increase of the simulated small-sized CM peak and with a lesser rate of decrease of the AeF CM AOD. Large GCT deviations from the AERONET retrievals were attributed to an extreme July, 2015 forest fire event.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".