Predicting the future hydrology of western Canadian Arctic watersheds dominated by thermokarst lakes
Bibliographic record
Abstract
Across extensive areas of the Arctic, watersheds have a myriad of lakes that cover up to 50% of the total surface area and can be linked together in complex streamflow networks.Warming of ice-rich permafrost has significant impacts on the interactions between surface water, shallow surface water, lakes, and streamflow.Most of these lakes formed from the melting of massive ground ice over the past millennia and are termed thermokarst lakes.Thermokarst lakes are susceptible to rapid, or catastrophic, drainage due to permafrost degradation, especially where ice-wedge polygons occur in low-lying areas adjacent to these lakes.These drainage events are increasing as of recent, for reasons unknown, and can create extreme floods that are a risk to people and infrastructure located downstream, and the destruction of fish habitat.To answer key questions related to this apparent crossing of a key tipping point in the viability of these lakes, this is a review of the new projects we have initiated at the Trail Valley Creek research station, north of Inuvik, NT, to investigate the controls on thermokarst lake drainage.We will use a combination of field observations, satellite data, remote sensing, and ultra high-resolution modelling focused on thermokarst lakes, ice-wedge polygons, and the impact of beaver activities, to answer key questions related to the history of lake drainage over the last 70 years and consider the future viability of these lakes.Insights gained from this study will help support climate change mitigation efforts for northern communities and ecosystems.1
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".