Improving Ice Segmentation in Permafrost Cores using Computed Tomography
Bibliographic record
Abstract
The segmentation of ice in X-ray Computed Tomography (CT) scans of permafrost samples has traditionally relied on the Hounsfield Unit (HU) thresholding approach while their accuracy is often limited by overlapping density ranges in complex and heterogeneous samples. Recent advances, including automated thresholding algorithms and machine learning techniques, offer improved precision by leveraging texture, contrast, and morphological features in CT images. This study investigates the evolution of ice segmentation methodologies by applying multiple approaches to a 164 cm long permafrost core drilled from a Yedoma upland in north-eastern Siberia. The core was analyzed using traditional HU thresholding, automated thresholding methods (e.g., Otsu and adaptive histogram-based segmentation), and machine learning models (e.g., random forests and convolutional neural networks). The results from CT scans and segmentation methods were validated and compared against laboratory measurements of ice content and density, ensuring a robust evaluation of each technique's accuracy and reliability.The results provide critical insights into the strengths, weaknesses, and suitability of different segmentation methods for permafrost cores. These findings contribute to the development of standardized, high-precision methodologies for non-destructive characterization of ice-rich soils, supporting geotechnical and climate change studies in permafrost regions.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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 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".