Characterisation of Geological Samples with Dual‐Energy <scp>XCT</scp>: A Comparison of Three Different Scanners
Bibliographic record
Abstract
The stoichiometric calibration method for dual‐energy computed tomography (DECT) can be used in geosciences to characterise materials based on their effective atomic number (Zeff) and their electron density (ρe) without previous knowledge of the incident X‐ray beam. A stoichiometrically calibrated DECT method was applied here to measure these two properties on three different sedimentary rocks using three different X‐ray CT instruments to determine which one is best to reveal the chemical composition or the mineralogical variations at the meso‐scale. The three tested instruments: (1) a medical CT, (2) a custom‐built micro‐CT, and (3) a commercial micro‐CT. Several acquisition settings were tested to identify the most suitable parameters for the characterisation the samples. Some parameters such as incident energies, resolution and calibration materials proved to have a significant impact on the accuracy of the characterisation. The determination of a general measurement protocol for geological samples was found to be difficult because of several complicating factors, including the nature of the sample, objectives of the study, and instrumental limitations that influence DECT characterisation. Nonetheless, comparison of the results obtained by the three scanners brings out the key parameters to be considered to perform a useful rock sample characterisation with DECT.
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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 | 0.002 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".