Geomorphology and landforms distribution in selected ice-free areas in the South Shetland Islands, Antarctic Northern Peninsula region
Bibliographic record
Abstract
Ice-free areas cover a small percentage of the land in the South Shetland Islands. However, they are significant as they contain ecosystems highly sensitive to environmental changes and are located within a region affected by global warming. These areas are dominated by periglacial, glacial, fluvial, and coastal processes and landforms, where permafrost is often present. Soil development is observed although vegetation cover is sparse and closely related to the geomorphology. The mapping and monitoring of ice-free areas is important as they are highly sensitive to climate change. The objective of this study was to characterize and map surface landforms in ice-free areas using traditional mapping methods as well as advanced remote sensing techniques. Geomorphological and topographical maps were initially obtained through field measurements and observations, and complemented with existing aerial photography at scales between 1:2000 and 1:25000. Thereafter, satellite-borne data became available and were included in the methodology to further determine the distribution of the landforms. In the Antarctic environment, Synthetic Aperture Radar (SAR) provides the most reliable images as data can be obtained in any weather conditions as well as during the day and night. Fully polarimetric SAR RADARSAT-2 were used to determine seven different terrain classes representing surface landforms in ice-free areas around Maxwell Bay (King George Island). The SAR remote sensing techniques were successfully applied to identify different periglacial, fluvial, glacial, coastal, as well as lithological landforms. Field data from Fildes Peninsula were used to train a supervised classifier to map further areas around Maxwell Bay. In this case, the ice-free areas around Maxwell Bay clearly show the dominance of periglacial landforms and processes. Therefore, these techniques can be used to compare past and future results and to monitor areas affected by changing environmental factors and increasing human activities.
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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 | 0.000 | 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 | 0.000 |
| 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 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".