Factors Influencing the Spatial and Temporal Occurrence of Thermo-erosional Landforms along the Yukon Coast, Canada
Bibliographic record
Abstract
Processes associated with permafrost degradation in the arctic coastal zone are highly dynamic and account for significant amounts of organic carbon released to the Arctic Ocean. Thermo-erosion, as a mechanism of rapid permafrost thaw, reshapes arctic landscapes and has a clear impact on the mobilization and distribution of carbon and nitrogen in permafrost terrains. However, few studies report on the diversity of thermo-erosional landforms or assess the factors involved in their development. \n \nThis study highlights the diversity of thermo-erosional drainage pathways -- including gullies and valleys -- and specific thermokarst features such as retrogressive thaw slumps and active layer detachments, and determines the prevailing factors accounting for their distribution and driving their expansion over the last 60 years along the Yukon coast. \nWith the software OrthoEngine from PCI Geomatica we used a large set of high resolution satellite images from 2011 (GeoEye-1 and WorldView-2) for geocoding aerial photographs from the 1950s. The aerial photographs come from the National Air Photo Library, Canada. This dataset allowed us to manually digitize and classify thermo-erosional gullies, valleys, retrogressive thaw slumps and active layer detachments for the 1950s and 2011 using ArcGIS 10.3. \nWe gathered additional observations during fieldwork in July and August 2015 on gully and valley morphologies, and on the current development stage of retrogressive thaw slumps. Based on remote sensing, we calculated and compared the surface area occupied by slumps in 1950s and in 2011 as well as the types, number and lengths of thermo-erosional drainage pathways over the same period. We coupled these information with additional datasets related to climate, geology and topography, and performed multivariate statistical analyses using the software R. \nOver this time span, we observed an important spatial heterogeneity in the landform dynamics among the different geological units. The number and the surface area of retrogressive thaw slumps increased on average. We did not detect a specific increase in the length of thermo-erosional drainage pathways over the whole area, however, in some specific geological units they decreased in length due to important coastal erosion. \n \nThis dataset will be complemented by soil organic carbon data collected across several thermo-erosional landforms during fieldwork conducted in 2015 in order to understand the processes of carbon mobilization within specific thermo-erosional landforms.
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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 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".