Multivariate Specifications in the Mineral Processing Field: An introduction
Bibliographic record
Abstract
The concept of multivariate specifications on incoming raw material was introduced in the mid-1990s in the chemical process industry as a tool to determine if a lot should be accepted from a supplier prior to its purchase. The objective of this paper is to adapt this concept to the mineral processing field using a simulation case study to assess the profitability of processing ore with different characteristics. To keep it simple, only the ore properties are considered to influence the final quality attributes: the concentrate flow rate and grade. Defining multivariate specification regions for raw ore properties is illustrated using simulated data from a grinding-flotation process where the feed ore average mineral grain size, grade and hardness are modified. This involves defining process performance classes based on an economic criterion, building a projection to latent structure PLS model, and adjusting statistical limits in the latent space of the model (i.e. the specification). The resulting specification region in the latent space based on a linear discriminant classifier allows to correctly classify 91% of the lots of ore in terms of their profitability.
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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".