The development of a modelling strategy for the simulation of fugitive dust emissions from in-pit quarrying activities: a UK case study
Bibliographic record
Abstract
Surface minerals extraction and processing operations can generate large quantities of fugitive dust that, when released in an uncontrolled manner, can cause widespread nuisance and potential health concerns for on-site personnel and surrounding communities. Typical fugitive dust emission sources may include minerals transfer points, conveyance, loading into crusher feed bins, haulage and blasting. To increase the understanding of the dispersion of fugitive dust from such activities it is necessary to develop suitable modelling strategies. The paper reports the results of a series of preliminary studies conducted using the UK Atmospheric Dispersion Modelling Software (ADMS3.1). A strategy was developed to model dust dispersion from blasting events and haul roads within a major UK limestone quarry. An analysis of the results confirmed the strong influence on the predicted dust dispersion of site-specific meteorological conditions and both the in-pit and surrounding terrain.
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How this classification was reachedexpand
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".