Drained lake basins on a circumpolar scale – Updates from the IPA Action Group
Bibliographic record
Abstract
Lakes and drained lake basins (DLB) are ubiquitous landforms in permafrost regions. The long-term dynamics of lake formation and drainage is evident in the abundance of DLBs covering 50% to 75% of arctic permafrost lowlands in parts of arctic Alaska, Russia, and Canada. Following partial or complete drainage events, DLBs evolve through time. As the basins age and ground ice enrichment occurs, the surface heaves and vegetation communities evolve, exhibiting spectral and texture differences indicative of these changing conditions. This mosaic of vegetative and geomorphic succession and the distinct differences between DLBs and surrounding areas can be discriminated and used to make a landscape-scale classification employing various indices derived from multispectral remote sensing imagery that, when combined with field sampling and peat initiation timing, can be used to scale across spatial and temporal domains. Previously published local and regional studies have demonstrated the importance of DLBs regarding carbon storage, greenhouse gas and nutrient fluxes, hydrology, geomorphology, and habitat availability. A coordinated pan-Arctic scale effort is needed to better understand the importance of DLBs in circumpolar permafrost-regions. Here we present an update of ongoing work within the Action Group on DLBs supported by the International Permafrost Association (IPA), an effort by the community to develop a first pan-Arctic drained lake basin data product. Comprehensive mapping of DLB areas across the circumpolar permafrost landscape will allow for future utilization of these data in pan-Arctic models and greatly enhance our understanding of DLBs in the context of permafrost landscapes. Utilizing remote sensing imagery (Landsat-8) and freely available DEM data sets (e.g. ArcticDEM) allows us to implement our mapping approach on a circumpolar scale. A previously published prototype of this data product covering the North Slope of Alaska forms the basis of this large-scale mapping effort. Here we present first result working towards a pan-Arctic remote sensing-based DLB data product focussing on selected areas in Canada and Siberia, Russia.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.015 | 0.005 |
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".