A process‐based model for quantifying the impact of climate change on permafrost thermal regimes
Bibliographic record
Abstract
Air temperature at northern high latitudes has increased at a higher rate than the global mean, and most general circulation models project that this pattern will continue. Climate warming can increase summer thaw depth and induce permafrost degradation, which may alter the dynamics and functions of northern ecosystems and the lifestyles of northern residents. To address these issues, we developed a process‐based model to simulate permafrost thermal regimes by combining the strength of existing permafrost models and land surface process models. Soil temperature and active layer thickness were simulated by solving the heat conduction equation, with the upper boundary conditions being determined using the surface energy balance and the lower boundary conditions being defined as the geothermal flux. The model integrated the effects of climate, vegetation, ground features, and hydrological conditions on the basis of energy and water transfer in the soil‐vegetation‐atmosphere system. The model was validated against the measurements at four sites in Canada. The simulation results agreed with the measurements of energy fluxes, snow depth, soil temperature, and thaw depth. These results indicate that this physically based model captured the effects of climate, vegetation, and ground conditions on soil temperature and freezing/thawing dynamics, and the model is suitable to investigate the impacts of transient climate change on soil thermal regimes and permafrost degradation and their consequent effects on ecosystem dynamics.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| 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".