Simulating the decadal‐ to millennial‐scale dynamics of morphology and sequestered carbon mobilization of two thermokarst lakes in NW Alaska
Bibliographic record
Abstract
Thermokarst lakes alter landscape topography and hydrology in widespread permafrost regions and mobilize significant permafrost carbon pools, including releasing methane (CH4) to the atmosphere. Despite this, the dynamics of lake evolution, permafrost thawing, and carbon mobilization are not well known. We present a 3‐D numerical model of thermokarst lakes on organic‐rich yedoma permafrost terrains with surface water flow and pooling naturally defining lakes that deepen, expand laterally, and drain due to talik formation, bank retreat, and both gradual and catastrophic drainage. We predict the 3‐D pattern of microbial methane production within the talik over time. As a first model test and calibration, beginning with small protolakes, we simulated 10,000 years of evolution of Pear and Claudi lakes, two neighboring thermokarst features on the northern Seward Peninsula, Alaska. Simulated lakes approximated observed bathymetry, but results are sensitive to initial topography and soil ice content. Local topography caused markedly different dynamics for the two lakes. Pear expanded rapidly across low‐relief topography, fully drained multiple times, and released little methane in later stages due to Pleistocene carbon depletion by the first and largest lake generation. Claudi grew slowly and continuously across high‐relief topography, forming high subaerial banks; partial drainages left remnant horseshoe lakes that continued to expand into virgin yedoma, mobilizing carbon at roughly the same rate irrespective of lake drainage. The ∼2× discrepancy between simulated CH4 production and observed emission rates in Claudi likely results from misestimation of hot spot ebullition, labile carbon content, CH4:CO2 production ratio, or microbial CH4 oxidation.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".