Random Forest Predictions of Fine Ash Concentration and Charging Processes From Experimentally Generated Volcanic Discharges
Bibliographic record
Abstract
Abstract Explosive volcanic plume rise is governed by the rate at which ambient air is ingested and heated by turbulent entrainment and mixing processes. A daunting observational challenge is to constrain the character of the underlying physical processes and their dependence on complex particle‐particle and particle‐gas interactions. Important clues may lie in the particle‐particle momentum exchange that gives rise to related electrical discharges near the vent during supersonic eruptions. Recent laboratory studies of positive and negative shock‐tube‐generated volcanic discharges show a correlation between fine ash concentration and the magnitude and number of positive discharges. Charge generation via collisions (triboelectrification) is hypothesized to be more efficient with high ash concentrations and at high decompression rates because collisions between particles become more frequent under these conditions. To understand the experimental data in greater detail, we train and implement a regression‐based random forest algorithm to quantitatively predict concentrations of fine ash using discharge count, magnitude and polarity as predictors. Using a metric for variable importance, we find in all, high pressure (HP, ≥10 MPa), and high ash mass experiments (HM, >22g) that positive discharge properties are most important when predicting fine ash concentration, consistent with triboelectrification as the predominant charging process under these conditions. This mechanism is not constrained for low‐pressure (LP, <10 MPa) conditions, suggesting a potential threshold decompression rate condition for this class of charging. This result provides insight into why near‐vent lightning is not a ubiquitous feature of explosive eruptions.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".