MineralMate: A standalone MATLAB-based aide for the magnetic separation of minerals
Bibliographic record
Abstract
MineralMate is a standalone MATLAB-based program designed to optimize the workflow associated with the magnetic separation of minerals. For nearly every bulk geochemical analysis some amount of mineral separation must occur, and the use of an electromagnetic separator is ubiquitous and considered as standard practice in many fields. Despite the commonality in which magnetic separation is used, there are considerable shortcomings. Electromagnet overheating and composite mineral grains are frequently encountered, as well as poorly constrained mineral behavior. These complications ultimately reduce the quality of downstream geochemical data. MineralMate is designed to alleviate these shortcomings by quickly and efficiently producing a magnetic separation workflow allowing the user to: (1) identify and compare optimal recovery ranges for different minerals from a bulk mineral assemblage, (2) identify the parameters on a conventional magnetic separator required to magnetically separate composite grains, (3) create/update user-specific magnetic susceptibility databases through empirical data collection, and (4) utilize an alternative magnetic separation equation.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.085 | 0.026 |
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".