Bibliographic record
Abstract
The Arctic is currently experiencing warming at much higher rates when compared to the global average, which has led to rapid changes within the cryosphere, including glacial retreat and permafrost thaw. These climate-induced processes are transforming proglacial environments, with ice-cored moraine thaw driving rapid landscape evolution. Although topographical changes caused by thawing ice-cored moraines have been studied in the Arctic, there is a lack of ice-cored moraine studies in central Spitsbergen. In this study, we present Unmanned Aerial Vehicle (UAV)-derived models of two glacial forefields in central Spitsbergen, Scott Turnerbreen (STB) and Longyearbreen (LYB), where six aerial photogrammetric surveys were flown over the course of three weeks, providing a high temporal resolution for three specific Retrogressive Thaw Slumps (RTS) and the forefields themselves. Previous aerial imagery captured in 2018 and satellite imagery gathered from 2014 allow for a greater range of temporal frequencies. Furthermore, using Ground Penetrating Radar (GPR), sites that experienced movement, as observed from the aerial surveys, were tested to determine if the cause of movement could be directly correlated to the melting ground ice. STB saw a loss of 67350m3 since 2018, with one-third of that volume loss being attributed to the three RTSs observed in this study. Since 2018, the LYB has lost 115,252m3 in volume, almost double the amount observed in STB. This disparity in lost material is evident in the area of the visible RTSs occurring in both study sites, with the LYB being home to an RTS almost 3 times the size of the largest RTS observed in STB. By integrating surface and subsurface analyses, this study provides a comprehensive understanding of ice-cored moraine dynamics under climate change, highlighting implications for geomorphological stability, sediment release, and hydrological systems. These findings emphasize the urgent need for continued monitoring and predictive modelling to assess the persistence of such changes in these Arctic proglacial landscapes.
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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 | 0.000 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".