Particle‐scale characterization of volcaniclastic dust sources within Iceland
Bibliographic record
Abstract
Abstract Volcaniclastic dust particles are characterized by unique physical properties, which are speculated to influence their rates of entrainment, emission and deposition within the atmospheric boundary layer. Few detailed particle‐scale measurements exist, so that natural particles often are idealized as solid glass spheres in the parameterization of dust dispersion models. This study shows that volcaniclastic dust particles from Iceland contain substantial quantities of amorphous glass, large internal voids and copious dustcoats comprised of nano‐scale flakes. Their high porosity, found to increase with particle diameter, generates particle densities that can be substantially lower than expected for a solid sphere. An abundance of volcanic glass also seems to increase particle porosity and roughness, and thereby strongly correlates with the Brunauer Emmett Teller surface area. An analysis based on Stokes' law further suggests that Icelandic dust with a standardized geometric diameter (10 μm or PM10), but with varying density, shape and origin, may have settling velocities in still air that are up to 20% lower than for a reference glass sphere. As a first approximation, neglecting complex particle interactions and wind speed, which also affect the deposition rate in the atmosphere, their low density and large surface area could increase the expected residence time by a factor of five. Model parameterization should be refined to incorporate these particle‐scale factors in order to improve on the estimation of volcaniclastic dust dispersion.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".