Physical and chemical properties of pollution aerosol particles transported from North America to Greenland as measured during the POLARCAT summer campaign
Bibliographic record
Abstract
Abstract. Within the framework of the POLARCAT-France campaign, aerosol physical, chemical and optical properties over Greenland were measured onboard the French ATR-42 research aircraft. The Lagrangian particle dispersion model FLEXPART was used to determine air mass origins. The study focuses particularly on the characterization of air masses transported from the North American continent. Air masses that picked up emissions from Canadian and Alaskan boreal forest fires as well as from the cities on the American east coast were identified and selected for a detailed study. Measurements of CO concentrations, aerosol chemical composition, aerosol size distributions, aerosol volatile fractions and aerosol light absorption (mainly from black carbon) are used in order to study the relationship between CO enhancement, ageing of the air masses, aerosol particle concentrations and size distributions. Aerosol size distributions are in good agreement with previous studies, even though, wet scavenging potentially occurred along the pathway between the emission sources and Greenland leading to lower concentrations in the aerosol accumulation mode. The measured aerosol size distributions show a significant enhancement of Aitken mode particles. It is demonstrated that the Aitken mode is largely composed of black carbon, while the accumulation mode is more dominated by organics, as deduced from aerosol mass spectrometric AMS and aerosol volatility measurements. Overall, during the campaign rather small amounts of black carbon from the North American continent were transported towards Greenland. An important finding given the potential climate impacts of black carbon in the Arctic.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.001 | 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".