A characteristic periglacial landform: Automated recognition and delineation of cryoplanation terraces in eastern Beringia
Bibliographic record
Abstract
Abstract Automated recognition and delineation of specific landforms and their constituent elements ranks among the most active areas of contemporary geomorphological research. This study contributes to that literature by applying semi‐ and fully automated recognition procedures to upland periglacial geomorphic landscapes. The Cryoplanation Terrace semi‐Automated Recognition (CTAR) algorithm utilizes basic terrain parameters to identify locations of cryoplanation terraces (CTs) from the high‐resolution ArcticDEM. Using a multistep process, candidate areas are identified based on morphometric characteristics. CTAR uses terrain derivatives to search ridges, hills, and mountains for flat areas bounded by abrupt breaks in slope. Because CTs are found exclusively in upland periglacial environments, some locations require that low‐lying areas be filtered out. To assess accuracy, CTAR was tested at five local study sites distributed across eastern Beringia, each containing multiple CTs delimited manually in a previous study. CTAR performed well, with an overall accuracy of 90%. A strong linear relationship exists between the size of CTAR‐delimited terraces and those identified in a previous study through air‐photo interpretation. In addition to identifying nearly all of the CTs in the five study areas, a fully automated version of the algorithm (GEE‐CTAR), implemented in Google Earth Engine, identified nearly 8,000 previously unmapped potential CTs in the Seward Peninsula region of western Alaska. The ability to identify CTs from digital elevation models provides a useful tool for recognizing and delineating upland periglacial topography. Objective recognition of large erosional landform elements created by periglacial processes is a critical step in developing the field of periglacial geomorphometry.
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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".