Feldspar minerals as efficient deposition ice nuclei
Bibliographic record
Abstract
Abstract. Mineral dusts are well known to be efficient ice nuclei, where the source of this efficiency has typically been attributed to the presence of clay minerals such as illite and kaolinite. However, the ice nucleating abilities of the more minor mineralogical components have not been as extensively examined. As a result, the deposition ice nucleation abilities of 24 atmospherically relevant mineral samples have been studied, using a continuous flow diffusion chamber at −40.0 ± 0.3 °C and particles size-selected at 200 nm. By focussing on using the same experimental procedure for all experiments, a relative ranking of the ice nucleating abilities of the samples was achieved. In addition, the ice nucleation behaviour of the pure minerals is compared to that of complex mixtures, such as Arizona Test Dust (ATD) and Mojave Desert Dust (MDD), and to lead iodide, which has been previously proposed for cloud seeding. Lead iodide was the most efficient ice nucleus (IN), requiring a critical relative humidity with respect to ice (RHi) of 122.0 ± 2.0% to activate 0.1% of the particles. MDD (RHi) 126.3 ± 3.4%) and ATD (RHi 129.5 ± 5.1%) have lower but comparable activity. From a set of clay minerals (kaolinite, illite, montmorillonite), non-clay minerals (e.g. hematite, magnetite, calcite, cerussite, quartz), and feldspar minerals (orthoclase, plagioclase) present in the atmospheric dusts, it was found that the feldspar minerals (particularly orthoclase) and some clays (particularly kaolinite) were the most efficient ice nuclei. Orthoclase and plagioclase were found to have critical RHi values of 127.1 ± 6.3% and 136.2 ± 1.3%, respectively. The presence of feldspars (specifically orthoclase) may play a significant role in the IN behaviour of mineral dusts despite their lower percentage in composition relative to clay minerals.
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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) | 0.001 | 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".