Ground ice estimation in permafrost samples using industrial computed tomography and multi-sensor core logging and comparison to destructive measurements
Bibliographic record
Abstract
Abstract. Permafrost contains a variety of ground ice types (e.g., pore, segregated, intrusive, vein, or massive ice) that have a diversity of cryotextures which organize to form distinctive cryostructures. The distribution and abundance of those ground ice types determine the potential for thaw subsidence and terrain effects of permafrost landscapes. Analysis of permafrost samples allows improved understanding of ground ice formation and internal and external permafrost processes, as well as improved tools to predict thaw settlement and consolidation. However, most methods to characterize permafrost are destructive and of low resolution. Here, some of the limitations of traditional destructive methods are overcome using an industrial computed tomography (CT) scanner. We use this laboratory-based method to systematically characterize five permafrost samples. We visualize cryostructures, measure frozen bulk density, and estimate volumetric and excess ice contents non-destructively and compare these results with traditional destructive analyses at similar spatial scales. The results show strong agreement between traditional destructive analyses (RMSEs for density, volumetric ice, and excess ice contents are 0.12 g cm−3, 3 %, and 6 %, respectively) as well as recent developments using a multi-sensor core logger (MSCL) (RMSEs for density and volumetric ice contents are 0.08 g cm−3 and 7 %, respectively). These results demonstrate that these non-destructive approaches can produce consistent results and provide the added benefit of archiving images and enhancing digital permafrost datasets. The development of standardized and interoperable methods for permafrost characterization has the potential to build more robust permafrost datasets and strengthen efforts to understand future thaw trajectories of permafrost landscapes.
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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".