Bibliographic record
Abstract
Mineralogical characterization applied to mineral processing is now widespread. The first step for a mineralogi- cal characterization study is usually size fractionation. Preparation of polished sections is done on size fractions to reduce complications in making representative cross sections of particles with large size differences. A sample is commonly fractionated into five or six size intervals. The drawback of this procedure is that it makes liberation studies more expensive, because one sample actually produces five or six sub-samples that need to be studied, i.e. one from each size interval. Thus to reduce cost of liberation studies, it would be desirable to study the un-sized sample. This paper provides a comparative liberation study of a set of samples both using size fractions and using the un-sized samples. The samples studied are the feed, the concentrate and the tails of a lead rougher flotation circuit. The results consistently show significant differences between the sized and the un-sized samples. Nevertheless, the results indicate that un-corrected liberation data from un-sized samples can be used for comparative studies that involve several related samples. Thus, it is possible to improve (or further understand) a concentrator circuit by using mineralogical data from un-sized samples around such circuit.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.002 | 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".