Multivariate Analysis of Volcanic Particle Morphology: Methodology and Application of a Quantitative System of Fragmentation Mechanism Classification
Bibliographic record
Abstract
Eruption mechanism plays a large part in the level of hazards a volcano can produce and can also have a tremendous effect on the climate when an eruption column reaches into the stratosphere.The purpose of this study is to refine and produce a methodology using Scanning Electron Microscopy (SEM) and image processing software to characterize ash particle morphology and use that to determine the fragmentation mechanism of any ash deposit.A quantitative method of determining ash morphology and linking it to eruptive styles can be applied to volcanic deposits worldwide and may be used to predict future hazards.The ash samples that are used in this study were collected from tephra deposits on Mount Erebus, Antarctica (< 15 ka), Mt.Redoubt, Alaska (2009), and Taupo, New Zealand (1.8 ka).The Taupo and Redoubt ash represent endmembers of phreatomagmatic and magmatically fragmented plinian eruptions, respectively.The fragmentation mechanism for the Mount Erebus ash is unknown but has been postulated to be a mixture of both phreatomagmatic and magmatic activity (surtseyan and strombolian, respectively).The ash was carefully hand-sieved to ~1mm diameter and imaged by SEM and then processed for morphological properties including rectangularity, circularity, compactness, elongation, solidity, etc.These morphological parameters were used in several statistical analysis to evaluate similarity and differences between deposits and to help constrain fragmentation mechanism.Discriminant analysis on all morphological parameters was found to be the best in separating the data and showing a linear trend between the two fragmentation mechanisms.The separation however, was only achieved after including parameters that are somewhat dependent I would like to thank many of the people who supported this project.First, thank you to my advisor, Dr. Kurt Panter, for all his guidance and encouragement throughout this project.Without his inquisitive nature, this project would not have expanded and grown the way it ultimately has.Also,
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".