Bibliographic record
Abstract
Geometallurgy combines geological and metallurgical information to create spatially-based predictive model for mineral processing plants. In the mines geometallurgy is executed through a geometallurgical program. The program goes through various stages and the most critical one is where samples are selected for the metallurgical testing. Paradoxically, the sample set should include the full variation within the ore body in terms of metallurgical response but this data is not available when samples are collected since it will be measured only afterwards. To overcome this problem mineralogical analyses or geometallurgical tests are recommended for every tenth ore sample. As an alternative a particle-based approach is presented. The particle-based approach uses minerals and particles to link the geological model with the process models. In the approach geological model contains quantitative information on modal mineralogy and mineral textures. This information is adequate for the process models to forecastwhat kind of particles will be generated in the comminution and how these particles will behave in the concentration unit processes. The particlebased approach is still a concept which needs further development for example in how the textural information is collected and used in the process model. Even it is regarded that Canada and Australia are the forerunners of geometallurgy Nordic Countries can show some tradition and recent investments in the area.
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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 | 0.001 | 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) | 0.000 | 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 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".