Dynamics of Glaciers and Ice Sheets at the Ocean Margin from Airborne and Satellite Data
Bibliographic record
Abstract
The modern contribution of glaciers and ice sheets to sea level rise increases with time and has largely been attributed to anthropogenic sources. The mass losses through the dynamic discharge of ice into the ocean has played a major role in the last two decades with a widespread acceleration of marine terminating glaciers. Recent studies have shown that these changes are closely linked to the ocean conditions, the bedrock and fjord topography. It is therefore crucial to document in details the evolution of glaciers and ice sheets, the bedrock topography and ocean properties to understand how and why the glaciers have been changing recently. Therefore, the aim of this thesis is to improve our understanding of ice dynamics and ice-ocean interaction by using a set of satellite and airborne remote sensing data over key regions. We describe the evolution of glacier dynamics and the detailed partitioning of the mass losses of the Queen Elizabeth Islands, Canada since the 1990s, which are major contributors to recent sea level rise. In Antarctica, we provide the first map of the sub-ice shelf bathymetry of the largest glaciers in the Amundsen Sea Embayment that reveals deep pathways for circumpolar warm water up to the grounding line of the glaciers. Finally, we assembled a comprehensive map of the bedrock and fjord topography of the southeastern coast of Greenland, and interpret the pattern of glacier retreat during the last 80 years, which was not possible before. The work proposed help to understand the recent deglaciation history of key regions in the Arctic, Greenland and Antarctica. The new mapping of sub-ice shelf, bedrock and fjord topography provides invaluable insights for mass balance calculation, ocean and ice-sheet modeling.
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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 | 0.002 |
| 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".