Bibliographic record
Abstract
Abstract. The chemical composition, sources, and concentrations of aerosol particles vary on a seasonal basis in the Arctic. While existing research has focused on understanding the occurrence of aerosol particles during the Arctic winter and spring, less is known of their occurrence during the Arctic summer. In this study, atmospheric aerosol particle chemical composition and concentration were determined during July–September 2018 at Tuktoyaktuk, NT, Canada (69.4∘ N, 133.0∘ W), to coincide with the Year of Polar Prediction's Second Special Observing Period in the Arctic. The chemical composition of fine (PM2.5) and coarse (PM10–2.5) aerosol filter samples suggests the ocean, mineral and/or road dust, and combustion were sources of the sampled aerosol particles. Mass concentrations of PM2 and PM10, estimated from optical particle counter measurements, remained within a similar range during the study. However, elevated mass concentrations coincided with a festival in the community of Tuktoyaktuk, suggesting local human activity was an important source of aerosol particles. Mass concentrations of PM2, which promote negative health effects in humans, were significantly lower at Tuktoyaktuk than the national air quality standard recommended by the government of Canada. These measurements provide an important baseline to compare with future measurements associated with the assessment of aerosol chemistry and air quality in 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 | 0.002 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.023 | 0.012 |
| Insufficient payload (model declined to judge) | 0.304 | 0.271 |
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".