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 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 | 0.005 | 0.011 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".