ECORE: A New Fast Automated Quantitative Mineral and Elemental Core Scanner
Bibliographic record
Abstract
Scarce platinum group elements (PGE) are mainly concealed in massive sulfides, and finding economically viable ore bodies largely relies on their fast chemical mapping. Most core scanners provide incomplete mineralogical contents, but none also provide a complete chemical analysis including light elements. This study investigates the performance of a fully automated laser-induced breakdown spectroscopy (LIBS) core scanner, the ECORE, by comparing its reliability to a scanning electron microscope-energy dispersive spectroscopy (SEM-EDS) mineral mapper and its speed to infrared diffuse reflectance hyperspectral imagers (IR-HSI). The LIBS elemental imaging has been put to the test in our previous work, as well as the high-resolution mineralogical mapping. This paper reports the scaling up analytical applicability of LIBS as a high performance and high-speed drill core scanner. The analysis of a full core tray in this study is the first and largest 7.62 megapixels image done by a LIBS core scanner to our knowledge. Both high-resolution and low-resolution data are put together to express both mineralogical and chemical content as a function of depth.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".