Vertical distribution of excess ice in icy sediments and its statistical estimation from geotechnical data (Tuktoyaktuk Coastlands, Northwest Territories)
Bibliographic record
Abstract
Excess ice can be found in the form of massive ice and within icy sediments and is an important variable to quantify as it strongly influences the geomorphic response of landscapes to permafrost thaw. The melting of excess ice in the Western Canadian Arctic has led to thaw subsidence and an increase in the number and size of thaw slumps observed across the Northwest Territories which cause issues to Northern infrastructure and affect fluvial and lacustrine watersheds. The Inuvik-Tuktoyaktuk Highway (ITH) is the first all-weather road to reach the Canadian Arctic Coast and its planning and construction has resulted in a significant cryostratigraphic dataset of 566 boreholes, which forms the basis of this contribution. Although visible ice is often recorded in boreholes, it is not a reliable measure of excess ice content on its own and there is currently no reliable method to estimate the excess ice content of boreholes based on commonly available geotechnical data. In this study, a 16-borehole subset of the ITH dataset for which samples were processed for volumetric excess ice content is used to train a beta regression model that predicts the excess ice content of stratigraphic intervals in the study area based on interval depth, visible ice content, surficial geology, and material types. The resulting predictions are compared to recorded massive ice intervals in the same boreholes and show that excess ice within icy sediments can significantly contribute to potential thaw strain and should be considered alongside massive ice when making thaw strain estimates.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| 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) | 0.001 | 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".