Laboratory Investigation of Particle‐Scale Factors Affecting the Settling Velocity of Volcaniclastic Dust
Bibliographic record
Abstract
Abstract Iceland contains some of the largest and most active dust sources in the Arctic, with the frequency of major emission events increasing in the wake of catastrophic volcanic eruptions, such as the 2010 Eyjafjallajökull eruption. Relatively juvenile Icelandic volcaniclastic dust particles are porous and highly angular, and they frequently bear ultrafine nanoscale dustcoats. Their low density is speculated to affect deposition rates in the atmosphere, while substantial amounts of water adsorbed onto their expansive surface area may affect nucleation, aggregation, and settling. Using a 2‐D laser Doppler anemometer, the present paper reports on a set of highly controlled fall column experiments designed to evaluate the dependency of the settling velocity upon particle morphology (e.g., size, shape, density, and surface area) and relative humidity (RH). Measurements for samples obtained from four Icelandic field sites are compared to those for solid glass spheres of similar diameter. The results confirm that the amount of adsorbed water increases with RH and the particle specific surface area. Higher dust concentrations are found to favor particle interaction and the formation of aggregates, especially at higher RH. Under dry conditions (20 % < RH < 30 % ), the horizontal drift velocity measured for the angular, porous Icelandic particles exceeds that observed for solid glass spheres, although these measurements are an order of magnitude smaller than the vertical velocity. As compared to particle diameter, shape does not appear to influence either the settling rate or drag coefficient at Reynolds numbers ≤10−2, owing to the dominance of viscous effects over form drag.
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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) | 0.000 | 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".