A study on low-grade copper sensor based-sorting
Bibliographic record
Abstract
Sensor-based sorting has been widely applied in the mineral industry due to the benefits offered such as the provision of selective ore to be processed in mineral processing plants.However, not all types of ores can be treated using sensor-based sorting because there are many factors in determining whether the ore can be sorted or not, such as heterogeneity and sensor response, which are more important to be evaluated.This paper evaluates the sortability of lowgrade copper ore and the sensor behavior, which in this case is X-Ray Fluorescence (XRF) and Electromagnetic (EM), based on heterogeneity and mass pull-grade-recovery correlation using the multivariable regression analysis.Laboratory work was conducted to understand the sensor behavior toward the heterogeneity of the ore using XRF and EM.They were used to scan the rocks as part of the particle sorting test and only XRF for the bulk sorting test.As a result, the XRF shows a better response toward the true recovery curves than EM sensors.In the case of EM, this sensor is not suitable to predict the grade as it is based on the magnitude and phase characteristics of the material.From the study, it was also found that liberation and particle size fraction play important role in producing a representative multivariable regression model which finally affects the mass pull, grade, and recovery.Both factors also determined the value Constituent or Distribution Heterogeneity.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".