Exploring the application of dual‐energy<scp>CT</scp>to discriminate sediment facies in a varved sequence
Bibliographic record
Abstract
Abstract Dual‐energy X‐ray computed tomography consists of imaging objects using two incident X‐ray beams of different energy to distinguish the different compounds within a sample based on their density (electron density, ρ e ) and elemental composition (effective atomic number, Z eff ). 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 more complex and heterogeneous sample, a varved sediment core 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 ρ e and Z eff 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. The paper not only eventually discusses 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.
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How this classification was reachedexpand
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 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".