Probabilistic modeling of explosibility of low reactivity dusts
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
This work presents probabilistic models to estimate dust explosion severity parameters of low reactivity dusts while capturing uncertainty in the parameter estimations. The marginally explosible behavior of combustible dusts has also been explored for different ignition energies and dust concentrations. Low-reactivity dusts are mostly characterized by low-K St values (i.e., K St < 45 bar . m/s in the 20-L chamber), also referred to as marginally explosible. These dusts pose a major problem regarding explosion classification due to the uncertainty they present on the industrial scale (i.e., explodes in the 20-L chamber but not in the 1-m 3 chamber, and vice versa). The proposed model has been used to study the explosibility of carbon black and zinc dust samples based on data generated in a 20-L Siwek chamber. The outcomes in terms of variability of maximum explosion pressure and maximum rate of pressure rise have been represented using maximum probable values and credible ranges. The likelihood of selected dusts exhibiting marginal explosibility characteristics at varying concentrations and ignition energies is also presented. The findings can be useful for making dust explosion safety decisions and facilitating risk reduction opportunities in the processing and handling of explosible dust.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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 it