Thaw lake expansion in a two‐dimensional coupled model of heat transfer, thaw subsidence, and mass movement
Bibliographic record
Abstract
Thaw lakes, widespread in permafrost lowlands, expand their basins by conduction of heat from warm lake water into adjacent permafrost, subsidence of icy permafrost on thawing, and movement of thawed sediment from lake margins into basins by diffusive and advective mass wasting. We describe a cross‐sectional numerical model with thermal processes and mass wasting. To test the model and provide an initial investigation of its utility, the model is driven using historical daily temperatures and permafrost conditions for the northern Seward Peninsula, Alaska (NSP; thick syngenetic ice, mean annual air temperature (MAAT) −6°C) and Yukon coastal plain (YCP; thin epigenetic ice, MAAT −10°C). In the model, lakes develop dynamic equilibrium profiles that are independent of initial morphology. These profiles migrate outward episodically and match the morphology of profiles from lakes that were measured at each site. Modeled NSP lakes expand more rapidly than YCP lakes (0.26 versus 0.10 m a−1) under respective modern climates. When identical climates are imposed, NSP lakes still grow more rapidly because their deeper basins and steeper bathymetric slopes move thawed insulating sediment away from the lake margin. In sensitivity tests, an increase of 3°C in MAAT causes 2.5× (NSP) and 1.6× (YCP) faster expansion of lakes. An 8°C decrease essentially halts expansion for both sites, consistent with paleostudies which attribute basins to postglacial warming. In the model, basins expand monotonically but lakes do not. The 1σ interannual variability of lake expansion is 0.51 (NSP) and 0.44 m a−1 (YCP), with single year rates of up to ±8 m occurring because of instabilities from thermal/mass movement coupling even under a stationary climate. This variability is likely a minimum estimate, compared to natural variability, and suggests that long measurement time series, of basins not lake surfaces, would best detect thermokarst acceleration resulting from a climate change.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".