Seasonal soil thawing processes on the Severnaya Zemlya archipelago
Bibliographic record
Abstract
The recent warming of the Arctic causes degradation of permafrost, release of greenhouse gases due to the decomposition of previously frozen organic matter, increase in the area and diversity of vegetation, and decrease of in the bearing capacity of permafrost soils. In this regard, the evolution of the seasonally thawed soil layer is of particular interest. The paper presents the results of comprehensive studies of energy exchange processes in the atmospheric surface layer and the upper layer of permafrost, carried out in 2016–2020 at the Research Station “Ice Base Mys Baranova” (Bolshevik Island, Severnaya Zemlya Archipelago), supplemented by the results of model calculations of seasonally thawed depth (STD) dynamics. The study examines the role of surface snow albedo decreases due to short-term intrusions of warm air masses, leading to the intensification of snow melting and soil surface heating due to increase in absorbed incoming solar radiation, is analyzed. A version of the Leibenson model, validated by data of observations, is used for assessing the role of landscape factors and meteorological conditions in the dynamics of STD. Despite the simplified formulation of the problem and the approximate assignment of heat and mass transfer of soil properties in the area under study, the model results could be considered satisfactory, and proposed approach can be used for assessing the state of STD.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".