Characterizing the Dynamics of Otto Glacier, Ellesmere Island, Canadian High Arctic: 1992-2020
Bibliographic record
Abstract
The mass loss observed from glaciers in the Canadian Arctic is unprecedented over recent decades (Hugonnet et al., 2021) and is the third largest contributor to global sea level rise (Derksen et al., 2019). One way in which glaciers lose mass to the ocean is through dynamic discharge, which involves the calving of icebergs to the ocean. Glacier dynamics in the Canadian Arctic have undergone limited study, especially surge-type glaciers, which oscillate between periods of fast flow and slow flow. Detailed studies of individual surge-type glaciers can enhance knowledge of how and why glaciers surge. As such, this thesis analyzed the surge cycle of Otto Glacier on northern Ellesmere Island in Nunavut, Canada, from 1992-2020. The analysis included velocity measurements from 1992-2020, which used data from optical and radar imagery. Three phases were identified for the study period: the fast flow phase (1992-2008), the deceleration phase (2009-2017), and the quiescent phase (2018-2020). Maximum velocities occurred within the lowermost ~6 km of the glacier during the fast flow phase (700-1300 m/yr), and minimum velocities (1-80 m/yr) were noted along the entire glacier during the quiescent phase. Terminus extent, analyzed with optical and radar imagery, advanced by 1545 m during the fast flow phase, and retreated by 1408 m by the end of the quiescent phase. Rates of glacier surface elevation change, obtained from pre-generated elevation products by Hugonnet et al. (2021), showed surface elevation lowering in the lowermost ~6 km of the glacier and thickening upglacier that was progressive over the study period. Analysis of bedrock topography found a v-shaped sill spanning ~4-8 km upglacier from the terminus, which was inferred to have influenced terminus retreat, glacier thickness, and subsequently velocity variability. The findings provide a detailed characterization of the surge cycle phases for Otto Glacier and suggest a possible surge mechanism, which has not previously been explored in depth.
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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 | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".