Prediction of truck productivity at mine sites using tree-based ensemble models combined with Gaussian mixture modelling
Bibliographic record
Abstract
In the past decade, machine learning (ML) algorithms have been widely applied to build prediction models for various mining applications. However, no research has been reported that forecasts truck productivity using ML algorithms. In this study, two tree-based ensemble learning algorithms, including random forest (RF) and gradient boosting regression (GBR), were proposed in combination with Gaussian mixture modelling (GMM) to train prediction models of truck productivity. GMM was adopted as a clustering technique to extract a latent variable from the training dataset. Multiple linear regression (MLR) and decision tree (DT) as single learning algorithms were used to construct prediction models to be compared with the tree-based ensemble models. The results showed that the tree-based ensemble models performed better than single models in predicting truck productivity with and without GMM clustering. Furthermore, GMM significantly increased the predictability of truck productivity prediction models by considering the latent variable. From the relative importance analysis, haul distance was the most influential factor among the observed input variables. Finally, the GMM-RF and GMM-GBR models with high accuracy were the proposed models for predicting truck productivity at mine sites.
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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 | 0.000 | 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".