Regional differences in drained lake basin distribution and surface characteristics across the Arctic 
Bibliographic record
Abstract
Lakes and drained lake basins (DLBs) cover 50% to 75% of the landscape in permafrost lowland regions of Alaska, Siberia, and Canada. Lakes and DLBs of different ages create a heterogeneous and dynamic mosaic of terrain units, providing unique habitats for flora and fauna. Lakes and drained lake basins play a crucial role in the permafrost landscape and ecosystem processes, influencing permafrost dynamics, the hydrologic regime, and biogeochemical processes including carbon cycling and greenhouse gas emissions. Depending on time passed since drainage of a given DLB, characteristics like surface roughness, vegetation, moisture, and abundance of ponds may vary between basins. Spatial heterogeneity within a single basin also varies between basins of different age, climatic subzone and underlying surficial geology. The mosaic of vegetative and geomorphic succession within DLBs and the distinct differences between DLBs and surrounding areas can be discriminated with remote sensing and used to derive a landscape-scale classification.Here we present an update from the circumpolar DLB mapping effort with a focus on regional differences in DLB distribution and DLB characteristics made visible by this systematic approach. We use the novel pan-Arctic assessment on DLB occurrence and the ESA Permafrost_cci circumpolar landcover unit data to assess the inter and intra-DLB spatial heterogeneity of surface characteristics, comparing different regions across the Arctic. Building on existing research, we utilize parameters like landcover patchiness, pond abundance and wetland distribution to infer relative age of different basins within a defined region. Different regions across the Arctic show different landcover distributions within basins, highlighting the importance for region-specific analysis when studying these landscape features. First results show distinct differences between DLBs of different geographic regions areas of differing surficial geology, based on the landcover occurring within basins and other surface properties. Comprehensive mapping and characterizing of DLBs on a circumpolar scale will allow for improved parametrization of regional to pan-Arctic modelling efforts and improve our understanding of DLBs as a crucial landform in Arctic permafrost landscapes.
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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 | 0.002 |
| 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".