Revisiting properties and concentrations of ice-nucleating particles in the sea surface microlayer and bulk seawater in the Canadian Arctic during summer
Bibliographic record
Abstract
Despite growing evidence that the ocean is an important source of ice-nucleating particles (INPs) in the atmosphere, our understanding of the properties and concentrations of INPs in ocean surface waters remains limited. We have investigated INPs in sea surface microlayer and bulk seawater samples collected in the Canadian Arctic during the summer of 2016. Consistent with our 2014 studies, we observed that INPs were ubiquitous in the microlayer and bulk seawaters; heat and filtration treatments reduced INP activity, indicating that the INPs were likely heat-labile biological materials between 0.22 and 0.02 µm in diameter; there was a strong negative correlation between salinity and freezing temperatures; and concentrations of INPs could not be explained by chlorophyll a concentrations. Unique in the current study, the spatial distributions of INPs were similar in 2014 and 2016, and the concentrations of INPs were strongly correlated with meteoric water (terrestrial runoff plus precipitation). These combined results suggest that meteoric water may be a major source of INPs in the sea surface microlayer and bulk seawater in this region, or meteoric water may be enhancing INPs in this region by providing additional nutrients for the production of marine microorganisms. In addition, based on the measured concentrations of INPs in the microlayer and bulk seawater, we estimate that the concentrations of INPs from the ocean in the Canadian Arctic marine boundary layer range from approximately 10 −4 to <10-6 L −1 at −10 ∘ C.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".