Long-term low-level Arctic aerosol trends, analysis, and climatological correlations at Alert, Canada
Bibliographic record
Abstract
Three decades of weekly winter low-level Arctic aerosol samples from Alert, Canada, are analyzed using Neutron Activation Analysis (NAA) in the TRIGA reactor at the University of Texas. The samples are from the longest currently-running Arctic aerosol data collection project and have received only limited analysis to date. The elemental composition (Aluminum, Bromine, Calcium, Chlorine, Copper, Iodine, Magnesium, Manganese, Sodium, Titanium, and Vanadium) is determined for each sample. The elemental results are characterized statistically and the results are compared to climatological data including temperature data, sea ice data, ice shelf data, and snow cover data. Positive Matrix Factorization (PMF) is performed on the complete data set to determine primary sources of the aerosol pollution. Other data from Alert, including Methanesulphonic Acid (MSA), Iron, and Sulphate data, is compared to the NAA results, and additional PMF is performed with the additional data. Results show many expected as well as unexpected trends and correlations including correlations with ice cover and temperature trends, correlations to decreasing anthropogenic pollution, and long-term trends of sea components and sea-component ratios in the aerosol. PMF results conclude that there are 5 predominant sources of the Arctic aerosol including two sea sources, two predominant anthropogenic sources (combustion and industrial), and a crustal component. This particular area of inquiry represents completely new information in the growing body of climate science and may influence studies that relate to the Arctic climate and environment, and should have an impact on the particular fields of Arctic Aerosol Monitoring, Atmospheric Transport, Global Diffusion and Dispersion, Arctic Climate Science, and Pollution Monitoring.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".