The effect of ore heterogeneity on the net value of a grinding/flotation product
Bibliographic record
Abstract
Ore heterogeneity (e.g., head grade, hardness, density and lithology) represents an important preoccupation for any given mine. The actual consequence of this variability is not always clear on the bottom line, especially when the average value remains constant over time. This paper examines the effect of random variations of two feed particle characteristics, namely the size index and grindability, on the net value of a grinding/flotation plant product. The simulated plant consists of rougher flotation cells processing the ore ground by a 2-stage semi-autogenous/ball mill circuit. Normal Gaussian distributions model the centered heterogeneity over time for three level of standard deviations (low, moderate and high) generating 32 possible scenarios. The high variability case reduces the product net value compared to the low variability one, equivalent to 1.5 M$/year loss. The additional revenues come from the ability to process more ore on average, while achieving the desired product size index in narrow variability situations. Conversely, high standard deviations lead to a slightly coarser ground product. The proposed simulation setting provides a quantitative basis to assess the financial benefits of regulatory control, and ore blending systems.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".