Investigation of three different river systems and floodplain areas in Arctic permafrost regions.
Bibliographic record
Abstract
Circumpolar permafrost landscapes are undergoing rapid transition and are strongly affected by climate warming. In these high latitude regions, the rising temperatures are disrupting the thermal equilibrium of the ground, influencing the Arctics moisture levels (wetting / drying) which is driving significant changes in hydrological regimes. The Arctic is a water-rich region with abundant freshwater systems. An important feature of large rivers is their discharge of globally significant quantities of freshwater, dissolved organic carbon, and other materials into the Arctic Ocean, while lakes and rivers also contribute to the global emission of carbon dioxide and methane to the atmosphere.For this study, three different Arctic river systems were investigated: the Mackenzie (Canada), the Ob (Russia) and the Lena (Russia). We mapped river systems at different levels of detail and localized floodplain-related areas by combining Digital Elevation Model (DEM) analysis with land cover maps (CALU). The floodplain areas are described by the fraction distribution of different land cover units. For example, the majority of the detected units showed water as the dominant unit. After filtering out the water areas, the remaining floodplain areas primarily consisted of wetland. We also separated the areas according to their bioclimate subzones (CAVM) which showed no significant differences in wet/dry units between the subzones; in all cases, wet areas were the majority.This work contributes to the mapping and characterization of rivers in the Arctic, with a focus on identifying, describing, and analyzing floodplains in relation to river systems. The results will enhance the understanding of Arctic hydrology, providing a foundation for further analysis of the wetting and drying in the Arctic which is the focus of the ERC project Q-Arctic.Datasets:CALU: Bartsch, A., Khairullin, R., Efimova, A., Widhalm, B., Muri, X., von Baeckmann, C., Bergstedt, H., Ermokhina, K., Hugelius, G., Heim, B., Leibman, M., & Gruber, C. (2024). Circumarctic Landcover Units (2.0) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.14235736 CAVM: Walker, D. A., Raynolds, M. K., Daniëls, F. J., Einarsson, E., Elvebakk, A., Gould, W. A., Katenin, A. E., Kholod, S. S., Markon, C. J., Melnikov, E. S., Moskalenko, N. G., Talbot, S. S., Yurtsev, B. A., and other members of the CAVM Team (2005). The Circumpolar Arctic vegetation map, J. Veg. Sci., 16, 267–282, https://doi.org/10.1111/j.1654-1103.2005.tb02365.x
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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".