Using dual-energy CT to discriminate sediment facies in a varved sequence 
Bibliographic record
Abstract
The attenuation of X-ray is influenced by the density (electron density, ρe) and the elemental composition (effective atomic number, Zeff) of the object being imaged. The incident X-ray beams energy controls the relative importance of these two properties in the resulting X-ray attenuation. Yet, dual-energy X-ray computed tomography, or using two incident X-ray beams of different energy, has been used in medical sciences to distinguish the different compounds within a sample based on their density (electron density, ρe) and elemental composition (effective atomic number, Zeff).An innovative approach, i.e., the stoichiometric calibration for dual-energy X-ray computed tomography, was already successfully implemented to identify single and homogeneous minerals easily and non-destructively. It is here applied for the first time to a varved sequence with three distinct facies. The output of dual-energy X-ray computed tomography was compared against elemental geochemistry obtained at the same resolution using a micro-XRF core scanner. The three individual facies can be successfully differentiated using dual-energy X-ray computed tomography because their range of ρe and Zeff values allow their discrimination. Correlations with elemental geochemistry are also discussed but are less conclusive, probably because of variations in grain size and porosity, and because these high-resolution analyses were not performed at the exact same location. We discuss the limitations when using dual-energy X-ray computed tomography on sediments but also demonstrates its potential to quantitatively study sediment cores in a non-destructive way.This presentation is based on https://doi.org/10.1002/dep2.27
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".