Spatio-Temporal Variation in High-Centre Polygons and Ice-Wedge Melt Ponds, Tuktoyaktuk Coastlands, Northwest Territories
Bibliographic record
Abstract
High-centred polygonal terrain is a widespread feature of Arctic landscapes that is sensitive to increasing ground temperatures because of its high ground-ice content. Understanding spatial variation in the distribution and sensitivity of high-centred polygonal terrain is important for predicting landscape change. In the Tuktoyaktuk Coastlands, Northwest Territories, Canada, mean annual ground temperatures in permafrost have increased between 1 and 2°C over the last 40 years and high-centred polygonal terrain comprises about 10 per cent of the terrestrial landscape. To investigate factors affecting the distribution and potential degradation of ice wedges, we mapped high-centred polygonal terrain and ice-wedge melt ponds, and documented ice wedge related thermokarst at anthropogenic disturbances using 2004 aerial photographs. Historical melt pond distribution was assessed using 1972 aerial photographs. The density of polygonal terrain (up to 37%) was significantly higher in the northern than the southern part of the study area, where more abundant lacustrine sediments and lower ground temperatures have favoured ice-wedge development. Larger proportional melt pond area (0.68%), increases in pond area (up to 3.74%) and a higher frequency of major thermokarst activity following anthropogenic surface disturbance (54%) suggest that high-centred polygonal terrain in the northern part of the study area is more susceptible to degradation than in the southern part. Copyright © 2016 John Wiley & Sons, Ltd.
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 | 0.000 | 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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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".