Comment on egusphere-2024-3379
Bibliographic record
Abstract
Abstract. Natural aerosol components such as particulate methanesulfonic acid (MSAp) play an important role in the Arctic climate. However, numerical models struggle to reproduce MSAp concentrations and seasonality. Here we present an alternative data-driven methodology for modeling MSAp at four High Arctic stations (Alert, Gruvebadet, Pituffik/Thule, and Utqiaġvik/Barrow). In our approach, we create input features that consider the ambient conditions during atmospheric transport (e.g., temperature, radiation, cloud cover, etc.) for use in two data-driven models: a random forest (RF) regressor and an additive model (AM). The most important features were selected through automatic selection procedures and their relationships with MSAp model output was investigated. Although the overall performance of our data-driven models on test data is modest (max. R2 = 0.29), the models can capture variability in the data well (max. Pearson correlation coefficient = 0.77), outperform the current numerical models and reanalysis products, and produce physically interpretable results. The data-driven models selected features related to the sources, chemical processing, and removal of MSAp with specific differences between stations. The seasonal cycles and selected features suggest gas-phase oxidation is relatively more important during peak concentration months at Alert, Gruvebadet, and Pituffik/Thule while aqueous-phase oxidation is relatively more important at Utqiaġvik/Barrow. Alert and Pituffik/Thule appear to be more influenced by processes aloft than in the boundary layer. Our models usually selected chemical processing related features as the main factors influencing MSAp predictions, highlighting the importance of properly simulating oxidation related processes in numerical models.
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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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.016 | 0.008 |
| Insufficient payload (model declined to judge) | 0.504 | 0.385 |
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".