Preprocessing Large Datasets Using Gaussian Mixture Modelling to Improve Prediction Accuracy of Truck Productivity at Mine Sites
Bibliographic record
Abstract
PreProcessing Large Datasets Using gaUssian MixtUre MoDeLLing to iMProve PreDiction accUracy of trUck ProDUctivity at Mine sitesThe historical datasets at operating mine sites are usually large.Directly applying large datasets to build prediction models may lead to inaccurate results.To overcome the real-world challenges, this study aimed to handle these large datasets using gaussian mixture modelling (gMM) for developing a novel and accurate prediction model of truck productivity.A large dataset of truck haulage collected at operating mine sites was clustered by gMM into three latent classes before the prediction model was built.The labels of these latent classes generated a latent variable.Two multiple linear regression (MLr) models were then constructed, including the ordinary-MLr (o-MLr) and the hybrid gMM-MLr models.The gMM-MLr model incorporated the observed input variables and a latent variable in the form of interaction terms.The o-MLr model was the baseline model and did not involve the latent variable.The gMM-MLr model performed considerably better than the o-MLr model in predicting truck productivity.The interaction terms quantitatively measured the differences in how the observed input variables affected truck productivity in three classes (high, medium, and low truck productivity).The haul distance was the most crucial input variable in the gMM-MLr model.This study provides new insights into handling massive amounts of data in truck haulage datasets and a more accurate prediction model for truck productivity.
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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 | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".