Identification of Common Minerals Using Stoichiometric Calibration Method for Dual‐Energy CT
Why this work is in the frame
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Bibliographic record
Abstract
Abstract Medical X‐ray computed tomography (CT) can be used to rapidly and non‐destructively characterize structure and density variations of geological specimens. More information about the nature of samples (electron density and elemental composition) can be retrieved using multi‐spectral approaches. This paper explores one of them, a stoichiometric calibration method for dual‐energy imaging, to identify the most common minerals. A set of 18 calibrating materials was selected to cover a range of variability in effective atomic number ( Z eff ) and electron density ( ρ e ) encountered in geological specimens. The validation of this calibration was performed analyzing 23 common minerals by mapping their respective Z eff and ρ e in order to identify the one with the closest properties. This study shows that the stoichiometric method correctly identifies the most important and common minerals (quartz, calcite, dolomite) that are usually not distinguishable using a single energy imaging method, although all the 23 studied minerals were not correctly determined. We show that this method previously elaborated for medical purposes is also efficient in earth science.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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 | 0.000 |
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 it