Methods to assess natural and anthropogenic thaw lake drainage on the western Arctic coastal plain of northern Alaska
Bibliographic record
Abstract
Thousands of lakes are found on the Arctic Coastal Plain of northern Alaska and northwestern Canada. Developed atop continuous permafrost, these thaw lakes and associated drained thaw lake basins are the dominant landscape elements and together cover 46% of the 34,570 km 2 western Arctic Coastal Plain (WACP). Lakes drain by a variety of episodic processes, including coastal erosion, stream meandering, and headward erosion, bank overtopping, and lake coalescence. Comparison of Landsat multispectral scanner (MSS) imagery from the mid‐1970s to Landsat 7 enhanced thematic mapper (ETM+) imagery from around 2000 shows that 50 lakes completely or partially drained over the approximately 25 year period, indicating landscape stability. The lake‐specific drainage mechanism can be inferred in some cases and is partially dependant on geographic settings conducive to active erosion such as riparian and coastal zones. In many cases, however, the cause of drainage is unknown. The availability of high‐resolution aerial photographs for the Barrow Peninsula extends the record back to circa 1950; mapping spatial time series illustrates the dynamic nature of lake expansion, coalescence, and drainage. Analysis of these historical images suggests that humans have intentionally or inadvertently triggered lake drainage near the village of Barrow. Efforts to understand landscape processes and identify events have been enhanced by interviewing Iñupiaq elders and others practicing traditional subsistence lifestyles. They can often identify the year and process by which individual lakes drained, thereby providing greater dating precision and accuracy in assessing the causal mechanism. Indigenous knowledge has provided insights into events, landforms, and processes not previously identified or considered.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 teacher head, 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".