Snow‐borne nanosized particles: Abundance, distribution, composition, and significance in ice nucleation processes
Bibliographic record
Abstract
Abstract Physicochemical processes of nucleation constitute a major uncertainty in understanding aerosol‐cloud interactions. To improve the knowledge of the ice nucleation process, we characterized physical, chemical, and biological properties of fresh snow using a suite of state‐of‐the‐art techniques based on mass spectrometry, electron microscopy, chromatography, and optical particle sizing. Samples were collected at two North American Arctic sites, as part of international campaigns (2006 and 2009), and in the city of Montreal, Canada, over the last decade. Particle size distribution analyses, in the range of 3 nm to 10 µm, showed that nanosized particles are the most numerous (38–71%) in fresh snow, with a significant portion (11 to 19%) less than 100 nm in size. Particles with diameters less than 200 nm consistently exhibited relatively high ice‐nucleating properties (on average ranged from −19.6 ± 2.4 to −8.1 ± 2.6°C). Chemical analysis of the nanosized fraction suggests that they contain bioorganic materials, such as amino acids, as well as inorganic compounds with similar characteristics to mineral dust. The implication of nanoparticle ubiquity and abundance in diverse snow ecosystems are discussed in the context of their importance in understanding atmospheric nucleation processes.
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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 | 0.000 | 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 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".