Ice nucleating particles in the atmosphere : laboratory and field studies
Bibliographic record
Abstract
Aerosol particles can indirectly affect climate by acting as ice nucleating particles (INPs). Although INPs are only a small subset of atmospheric particles, they can have a significant impact on the hydrological cycle and climate by initiating ice formation in clouds and by modifying the lifetime and optical properties of clouds. Nevertheless, the properties of atmospheric INPs are not yet fully understood. Two important types of atmospheric INPs are mineral dust and biological particles. This dissertation focuses on these two types of INPs. During atmospheric transport, mineral dust particles can acquire water-soluble coatings, such as coatings containing alkali metal nitrates, inorganic acids, and organic solutes. As a result, the effects of alkali metal nitrates, inorganic acids, polyols, and carboxylic acids on the ice nucleation properties of potassium-rich feldspar (K-feldspar), a type of mineral dust INP in the atmosphere, were examined. In addition, daily INP concentrations at Alert, Nunavut, a ground site in the Canadian High Arctic, were determined for October and November of 2018, and the contribution of mineral dust and biological particles to the total INP population was evaluated for this location and time period. The results in this dissertation improve our understanding of the properties of mineral dust INPs under atmospheric conditions, as well as the concentration, composition, and source of INPs in the Arctic. This information should be useful for global and regional climate models.
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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 | 0.001 | 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 | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".