Using ArcticDEM to identify and quantify pan-Arctic retrogressive thaw slump activity
Bibliographic record
Abstract
With the increased availability and coverage of high resolution satellite imagery, characterizing processes at the pan-Arctic scale is now possible. This baseline pan-Arctic product will enable us to highlight areas for future research efforts and to standardize observations that are currently locally or regionally focused. The ArcticDEM project (www.arcticdem.org) has released a large collection of 2 meter resolution Digital Elevation Models (DEMs) for all land areas above 60 °N. These DEMs are created using high resolution (~0.5 m) stereo paired satellite images (by DigitalGlobe and include Worldview- 1 (launched 2007), 2 (2009), 3 (2014) and GeoEye-1 (2008) satellites). Using repeat DEMs, we are developing algorithms for automated detection to identify and quantify land surface topographic changes from Arctic volcano eruptions and mass wasting events to create a pan-Arctic mass wasting inventory, including retrogressive thaw slumps. Currently, retreat rates reported for retrogressive thaw slumping activity differ between studies, and our dataset will enable rates to be standardized for slump activity after 2007. Furthermore, our mass wasting inventory will enable us to investigate the triggers of mass wasting events and to analyze the linkages to the contributing factors including climate, topography, and geology. We will be presenting preliminary results focusing specifically on retrogressive thaw slumps, including time series analysis for topographic change detection and using field observations for validation. We welcome collaborators who can share the field or remote sensing observations to aid in our validation efforts.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".