Predictive Models of Mineralogy from Whole-Rock Assay Data: Case Study from the Productora Cu-Au-Mo Deposit, Chile
Bibliographic record
Abstract
Abstract Mineralogy is a fundamental characteristic of a given rock mass throughout the mining value chain. Understanding bulk mineralogy is critical when making predictions on processing performance. However, current methods for estimating complex bulk mineralogy are typically slow and expensive. Whole-rock geochemical data can be utilized to estimate bulk mineralogy using a combination of ternary diagrams and bivariate plots to classify alteration assemblages (alteration mapping), a qualitative approach, or through calculated mineralogy, a predictive quantitative approach. Both these techniques were tested using a data set of multielement geochemistry and mineralogy measured by semiquantitative X-ray diffraction data from the Productora Cu-Au-Mo deposit, Chile. Using geochemistry, samples from Productora were classified into populations based on their dominant alteration assemblage, including quartz-rich, Fe oxide, sodic, potassic, muscovite (sericite)- and clay-alteration, and least altered populations. Samples were also classified by their dominant sulfide mineralogy. Results indicate that alteration mapping through a range of graphical plots provides a rapid and simple appraisal of dominant mineral assemblage, which closely matches the measured mineralogy. In this study, calculated mineralogy using linear programming was also used to generate robust quantitative estimates for major mineral phases, including quartz and total feldspars as well as pyrite, iron oxides, chalcopyrite, and molybdenite, which matched the measured mineralogy data extremely well (R2 values greater than 0.78, low to moderate root mean square error). The results demonstrate that calculated mineralogy can be applied in the mining environment to significantly increase bulk mineralogy data and quantitatively map mineralogical variability. This was useful even though several minerals were challenging to model due to compositional similarities and clays and carbonates could not be predicted accurately.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".