Quantitative Inversion Modeling Method for Grading Deerni Copper Deposits Based on Visible and Near-Infrared Hyperspectral Data
Bibliographic record
Abstract
Quantitative metal grade inversion based on hyperspectral data is an effective approach to achieve the real-time in situ determination of ore body grades and has the advantages of low cost compared with traditional chemical analysis methods. However, the redundant nature of hyperspectral data and the parameter-limiting nature of machine learning algorithms reduce the modeling accuracy and precision, resulting in severe limitations on the application of hyperspectral techniques for the grade inversion of Deerni copper ore bodies. In this paper, we first obtained visible-NIR hyperspectral data for 190 ore samples using a spectrometer and determined the copper content of the sample set using chemical analysis; then, we processed the raw hyperspectral data using three dimensionality reduction algorithms and optimized a BP neural network based on an evolutionary algorithm. Finally, a Deerni copper grade inversion model was established using the hyperspectral data before and after dimensionality reduction, and the inversion accuracy and precision was compared and analyzed with that obtained by the BP neural network, the random forest and the variable hidden layer nodes models. The combination of the LLE dimensionality reduction algorithm and the optimized BP neural network algorithm achieves the highest modeling precision, with an R2 of 0.950.
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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 | 0.001 | 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".