Origin and polycyclic behaviour of tundra thaw slumps, Mackenzie Delta region, Northwest Territories, Canada
Bibliographic record
Abstract
Abstract In tundra uplands east of the Mackenzie Delta, retrogressive thaw slumps up to several hectares in area typically develop around lakes. Ground temperatures increase in terrain affected by slumping due to the high thermal conductivity of exposed mineral soils and deep snow accumulation in winter. Mean annual temperatures at the top of permafrost were several degrees warmer in thaw slumps (−0.1°C to −2.2°C) than beneath adjacent undisturbed tundra (−6.1°C to −6.7°C). Simulations using a two‐dimensional thermal model showed that the thermal disturbance caused by thaw slumping adjacent to tundra lakes can lead to rapid near‐surface lateral talik expansion. Talik growth into ice‐rich materials is likely to cause lake‐bottom subsidence and rejuvenation of shoreline slumping. The observed association of thaw slumps with tundra lakes, the absence of active slumps on the shores of drained lakes where permafrost is aggradational and depressions in the lake bottom adjacent to thaw slumps provide empirical evidence that thermal disturbance, talik enlargement and thawing of subadjacent ice‐rich permafrost can drive the polycyclic behaviour (initiation and growth of slump within an area previously affected by slumping) of lakeside thaw slumps. Copyright © 2009 John Wiley & Sons, Ltd. and Her Majesty the Queen in right of Canada.
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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 | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".