Investigating dust explosibility using exploratory data analysis
Bibliographic record
Abstract
Dust explosibility using exploratory data analysis on explosibility testing data and physico-chemical parameters of dust samples was investigated. The analysis was founded on explosion severity testing data, key influencing factors ( e.g ., particle size distribution prior to dispersion, nominal dust loading, inter-particle spacing, and number particle density in the explosion chamber), and relevant physical and chemical parameters, including density, thermal conductivity, specific heat capacity, and specific heat of reaction . The correlation coefficients of selected factors and influencing features for the maximum explosion pressure and maximum rate of pressure rise were computed and visually depicted for the given dust samples. For better understanding via simpler representation in 2D-space, the resulting high-dimensional datasets from dust explosibility testing and physico-chemical properties were processed using an unsupervised data-driven technique— namely, principal component analysis, t-distributed stochastic neighbor embedding, and uniform manifold approximation and projection. By providing statistical analyses to understand the interdependence of key physico-chemical parameters influencing dust explosibility, this work can help in further exploration of dust explosions and devising strategies for safer handling and processing of different dusts.
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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 | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".