Estimating Particle Size Distribution of Mine Dump Materials using CenterMask Neural Network
Bibliographic record
Abstract
Particle size distribution potentially affects the shear strength of mine overburden dump materials.Estimating particle sizes at the dump site is crucial to determine the shear strength of dump materials.Delineation of particle sizes from dump images possesses challenges due to the variations in size, shape, color, texture and granularity.Increasing computational capabilities and advancements in Artificial Intelligence-based algorithms facilitate to apply the neural networks to solve industry problems.In the present study, a novel application of a deep neural network is proposed to generate segmentation masks over the dump particles.CenterMask network is trained on a manually labelled and annotated dump dataset.The model's performance is evaluated by comparing the predicted segmentation masks with the ground truth data.The trained model predicts the fine particles as less than as five millimetres to the maximum present in the dump image.The accuracy of CenterMask is compared with Mask R CNN, a popular instance segmentation algorithm on the dump dataset.The coordinates of predicted segmentation masks are used to determine the particle size distribution curves.A web application is developed to generate particle size distribution curves.The model's accuracy and inference time make it suitable as a quick and reliable source of estimating the sizes of particles.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".