Time‐dependent morphology of thaw lakes and taliks in deep and shallow ground ice
Bibliographic record
Abstract
The shape and depth of thaw lake basins depends on lake age, on whether the talik (thaw bulb) is at steady state, and on the distribution of ice in the ground. To investigate implications of this broad hypothesis, we use a numerical model of conductive heat transfer, phase change, and thaw subsidence of ice‐rich sediment beneath a lake in cross section. For ground thermal properties with lake temperatures and dimensions consistent with measurements, modeled talik depth approximately increases with except in lakes in deep ground ice which deepen more rapidly because of consolidation on thawing. In deep ground ice environments, basins achieve depths of ≈20 m in ≈5 ka. Expanding lakes with disequilibrium taliks have deep basins with broad, 100+ m wide, inclined margins. In shallow ground ice settings (either original epigenetic ice or in permafrost that has reformed in drained basins), lakes are <3 m deep and flat bottomed. Unaffected by preexisting topography and ground ice variations, first‐generation lakes in deep ground ice are rounder and grow larger in area than later‐generation lakes. These predictions are consistent with GPS, sonar, and remote sensing measurements of bathymetry and plan view shape of first‐ and later‐generation lakes in substrates with deep syngenetic ground ice (Pleistocene loess, northern Seward Peninsula, Alaska) and shallow epigenetic ice (Yukon Arctic coastal plain).
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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 | 0.000 |
| 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 | 0.001 | 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".