Terrain Controls and Landscape-Scale Susceptibility Modelling of Active-Layer Detachments, Sabine Peninsula, Melville Island, Nunavut
Bibliographic record
Abstract
Modelling the susceptibility of permafrost slopes to disturbance can identify areas at risk to future disturbance and result in safer infrastructure and resource development in the Arctic. In this study, we use terrain attributes derived from a digital elevation model, an inventory of permafrost slope disturbances known as active-layer detachments (ALDs) and generalised additive modelling to produce a map of permafrost slope disturbance susceptibility for an area on northern Melville Island, in the Canadian High Arctic. By examining terrain variables and their relative importance, we identified factors important for initiating slope disturbance. The model was calibrated and validated using 70 and 30 per cent of a data-set of 760 mapped ALDs, including disturbed and randomised undisturbed samples. The generalised additive model calibrated and validated very well, with areas under the receiver operating characteristic curve of 0.89 and 0.81, respectively, demonstrating its effectiveness at predicting disturbed and undisturbed samples. ALDs were most likely to occur below the marine limit on slope angles between 3 and 10° and in areas with low values of potential incoming solar radiation (north-facing slopes). Copyright © 2016 John Wiley & Sons, Ltd.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".