Permafrost dynamics and their hydrologic impacts over the Russian Arctic drainage basin
Bibliographic record
Abstract
Permafrost is an important component in hydrological processes because changes in runoff over the Arctic drainage basin cannot be well explained by changes in precipitation-related variables. However, current understanding of the influences of permafrost on hydrological dynamics is insufficient. This study investigated historical variations in permafrost conditions and their potential hydrologic effects over the Russian Arctic drainage basin. The results show that soil temperature (at 0.40 m below surface) has increased about 1.4 °C over the Ob, 1.5 °C over the Yenisei, and 1.8 °C over the Lena River basin from 1936 through 2013, possibly resulted in a significant thawing of permafrost. Rapid active layer changes have occurred since the 1970s. The volume of the active layer increased by 28, 142, and 228 km3 over the Ob, Yenisei, and Lena basins, respectively, since the 1970s. Melting ground ice caused by deepening active layer may be a limited contribution to annual runoff. Runoff during freeze season (October–April) showed significant positive correlations (p < 0.05) to active layer thickness in the Yenisei and Lena basins while negative correlation (p > 0.05) in the Ob basin. These results imply that, in basins with high permafrost coverage, a deeper active layer increased soil water storage capacity and perhaps contribute to an increase in winter runoff.
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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 | 0.001 | 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) | 0.000 | 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".