Terrestrial and marine sources of ice nucleating particles in the Eurasian Arctic
Bibliographic record
Abstract
Ice nucleating particles (INPs) catalyze primary ice formation in Arctic low-level mixed-phase clouds, influencing their persistence and radiative properties. Knowledge of the abundance and sources of INP over the remote Arctic Ocean is scarce due to limited data coverage, particularly in the Eurasian Arctic. This study presents summertime measurements of INP concentrations in seawater, fog water and air from the ship-based Arctic Century Expedition, exploring the Barents, Kara, and Laptev Seas, and the adjacent high Arctic islands and archipelagos in August and September 2021. Heat sensitivity tests of ambient aerosols revealed that heat-liable, biogenic INPs make up the majority of Arctic INP populations at temperatures above -20 °C, and to a lesser extent down to -25 °C. INP content in fog water is found to be similar to ambient aerosol, indicating that INP in marine air could also act as cloud condensation nuclei. Measurements of aerosolized INPs using an on-board sea-spray aerosol bubble tank generator exhibit a positive correlation with ambient INP concentrations, but not with INP abundance in seawater samples. INP concentrations in air derived from sea water samples (using a NaCl conversion factor representative for the Arctic) were significantly lower than those measured in ambient air or bubble tank experiments. INP concentrations in bubble tank experiments positively correlated with the phosphate and fluorescence signals in the water. This suggests an important role of the aerosolization mechanism for preferentially partitioning biogenic INPs to the atmosphere.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".