Spectroscopic characterisation of feedstock for copper smelters by machine-learning
Bibliographic record
Abstract
A flame-emission spectrometer was built to determine the elemental composition of powdered minerals that are important in copper smelting processes. The feedstock, consisting of milled concentrate, was fed into an oxyacetylene flame without sample preparation. The elemental composition (Cu, Fe, S, Si, and Zn) was determined by applying an artificial neural network (ANN) to a set of emission spectra obtained from Cu and Fe pure elemental powders, five pure mineral powders of known composition and 30 binary mixtures of these mineral samples. The ANN model was able to accurately predict the Cu and Fe content of these mineral powders within better than 2% of the value obtained from ICP-OES. The analysis was repeated on 12 industrial samples with well-known compositions. Spectra from these samples were analyzed both in isolation of the reference minerals and together with the reference minerals, giving similar results.
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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) | 0.006 | 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".