Evidence of sea ice source in aerosol sulfate loading and size distribution in the Canadian High Arctic from isotopic analysis
Bibliographic record
Abstract
Abstract The influence of frost flowers and seawater brine on ion chemistry in snow, snowpack, ice cores, and aerosols is detected when a lower sulfate to sodium ratio than in seawater is present in polar regions. This evidence can be masked when large amounts of non‐sea‐salt sulfate are present from other sources such as biogenic and anthropogenic sulfate. Frost flower δ34S values were measured for the first time in frost flower sulfates and did not differ significantly from the sea salt δ34S values of +21‰. A method using stable isotopes is introduced to determine the limit of contributions from sea salt and sea ice sources (including frost flowers and brine) on sulfate concentrations in aerosol samples from Alert, Canada. Knowledge of the range of values of δ34Snss and the SO4/Na ratio found in sea ice sources (i.e., frost flowers) is used to quantitatively constrain the contributions from frost flowers and sea salt in the Arctic aerosol mass during the onset of winter in 2007 and 2008, allowing for quantification of non‐sea‐salt sulfate amounts during times when frost flowers are present. Frost flower and/or brine influence was found predominantly in the coarse‐mode aerosols (>0.95 µm). This method to determine the contributions from sea salt and sea ice sources can be carried over to future studies with snow and ice cores.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 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".