Customized Spectral Libraries for Effective Mineral Exploration: Mining National Mineral Collections
Bibliographic record
Abstract
Abstract Infrared (Visible-Near Infrared-Shortwave Infrared (VNIR-SWIR)) spectroscopy is a cost-effective technique for mineral identification in the field. Modern hand-held spectrometers are equipped with on-board spectral libraries that enable rapid, qualitative analysis of most minerals and facilitate recognition of key alteration minerals for exploration. Spectral libraries can be general or customized for specific mineral deposit environments. To this end, careful collection of spectra in a controlled environment on pure specimens of key minerals was completed using the National Mineral Reference Collection (NMC) of the Geological Survey of Canada. The spectra collected from specimens in the ‘Kodama Clay Collection’ were processed using spectral plotting software and each new example was validated before being added to a group of spectra considered for incorporation into the on-board library of the handheld ASD-TerraSpec Halo near-infrared (NIR) mineral identification instrument. Spectra from an additional suite of mineral samples of the NMC containingREE, U, Th, and/or Nb are being prepared for a new, publicly available spectral library. These minerals commonly occur in carbonatite or alkali intrusive deposits, and as such will assist in the exploration for critical metals.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.023 | 0.019 |
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".