The influence of bedrock geology on glacier dynamics in the St. Elias Mountains, Yukon, Canada
Bibliographic record
Abstract
Glacier surges are characterized by order-of-magnitude increases in flow that can be sustained for months to years, facilitated by a dramatic increase in basal water pressure that permits rapid sliding. An explanation for the non-random geographical distribution of surge-type glaciers and the underlying causes of surges remain the source of speculation. Glacier surges are dynamic end-members of glacier behaviour that showcase fundamental processes operating under all warm-based glaciers. Providing an explanation for the distribution and mechanisms of surging will allow us to better predict the role and responses of glaciers in a warming climate. The primary objective of this research is to understand the relationship between geological substrates and surge-type glaciers. A second objective is to understand the more general relationships between bedrock properties and the physical and chemical processes of glacial erosion. Using data from 11 surge-type and 9 non-surge-type glaciers in the St. Elias Mountains of Yukon, Canada, I investigate geological variables that represent system inputs, such as bedrock mineralogy and fracture characteristics, and system outputs such as meltwater chemistry and the grain size and mineralogy of proglacial river suspended sediments. I find that glacier surging is correlated with bedrock fracture spacing and the grain size of suspended sediments. I propose that bedrock fracture spacing controls the rate of clast production, and therefore the distribution of a clast-rich till-transition zone, which provides the excess friction necessary for the development of an ice reservoir prior to surging. Within a given climate envelope and mass-balance regime, this conceptual model can help to explain the geographical distribution of surge-type glaciers. Through a mineralogical analysis of electrically fragmented bedrock samples and proglacial suspended sediment samples, I observe that primary minerals are comminuted to sub-micron sizes, and grain rounding appears to be shaping medium-silt size grains and smaller. Finally, I find that chemical alteration of sediment and clay mineral precipitation could be mechanisms to explain the characteristically low silica in glacier meltwaters. Through this work, I have highlighted some of the ways in which the geological substrate can drive subglacial physical and chemical erosion and thus, glacier dynamics.
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 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".