Relative contributions of anthropogenic emissions to black carbon aerosol in the Arctic
Bibliographic record
Abstract
Using a global air quality model with online aerosol algorithm GEM‐AQ, this work first validates the performance of the model against available observations and then estimates the regional contributions to the Arctic black carbon (BC) aerosol from anthropogenic sources. The comparisons against the surface measurements at Alert and Zeppelin suggest that the Arctic BC aerosol can be predicted by the model within 15% on annual average, and the seasonality of Arctic BC is predicted by 90% and 66% at these sites, respectively. Comparisons against surface measurements in North America, Europe, and South Asia confirm that surface BC concentrations are reproduced by the model within a factor of 2 at most (104 out of 115) sites investigated. Using GEM‐AQ, sensitivity experiments are conducted by reducing the anthropogenic emissions from selected regions by 20%. Based on area‐weighted results for the Arctic region, model simulations suggest that Europe contributes more (up to 57%) to the lowest 5 km of the Arctic troposphere than any other region. The contribution of Asian Russia is significant near the surface (about 30% at 100 m above the surface) and decreases rapidly to less than 10% at the altitude of about 5 km in the Arctic troposphere. The contributions from South and East Asia increase with increasing altitude, and become more significant than others in the upper troposphere and the lower stratosphere, with their peak contributions of about 35% and 40%, respectively. North American contribution to the Arctic troposphere (about 10–20%) has the least variations in the vertical direction among the potential source regions affecting the Arctic.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".