Measuring varve thickness using µCT: a comparison with thin section
Bibliographic record
Abstract
Abstract. X-ray micro-computed tomography (μCT) scans were performed on four varved sediment cores collected in Grand Lake (Labrador) and previously studied with thin sections. These scans allowed to investigate the possibility of using µCT as a substitute for thin sections to carry out counts and thickness measurements of varved sediments. Comparing varve counts of these two methods, μCT counts are slightly higher than the ones made with thin sections. The difference in counts suggests that the petrographic study and a SEM analysis of a thin section remain necessary for determining the varve character of the laminae. Yet, µCT allows measurements in multiple directions, improving the robustness of the counts and allowing avoiding the manufacturing of continuous thin sections along sediment sequence. As to the thickness measurement, the µCT analyses were made in two perpendicular directions. Not surprisingly, measurements made on the same cutting plane as the thin section are quite similar to the ones made on the latter. However, there are significant differences with measurements made on the perpendicular plane. This highlights the need to perform varve thickness measurements in at least two perpendicular directions for better estimates of varved sediment thicknesses. In addition, the study illustrates that µCT is an effective way to select the least deformed zones with parallel varves to carry out the best possible thickness measurements.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".