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Record W7039701432

Modelling the Subglacial Hydrology of Trinity-Wykeham Glaciers of the Northern Canadian Arctic

2023· dissertation· en· W7039701432 on OpenAlexaboutno aff

Bibliographic record

VenueUWSpace (University of Waterloo) · 2023
Typedissertation
Languageen
FieldEarth and Planetary Sciences
TopicCryospheric studies and observations
Canadian institutionsnot available
Fundersnot available
KeywordsMeltwaterGlacierArcticGlacial periodCryosphereGlacier ice accumulationGlacier morphologyIce sheetIce capsSea ice
DOInot available

Abstract

fetched live from OpenAlex

In Canada’s High Arctic region, the melting of glacial ice contributes substantially to the world’s increasing sea levels (Harig and Simons, 2016). Of the icebergs in the region, approximately 62% are discharged by Trinity-Wykeham Glaciers. In the last twenty years, these glaciers have retreated approximately 5 km, also doubling in speed and tripling in iceberg production during that period (Van Wychen et al., 2016). It is argued that warming air temperatures are the primary cause of glacial retreat and consequently sea level rise in the Canadian High Arctic region (Cook et al., 2019). This is because the warmer air causes ice on the surface of glaciers to melt at a higher rate. This meltwater is routed to the base of the ice, where it can directly impact the glacier’s velocity. If the water spreads into high-pressure cavities over a large enough area, the ice can often accelerate. However, if the water input is great enough to form large, low-pressure channels at the base of the glacier, water will be drawn from the higher-pressure regions and the ice will often decelerate (Iken and Bindschadler, 1986). \n \nThe Glacier Drainage System (GlaDS) subglacial hydrology model (Werder et al., 2013) is used to examine the development of hydrological networks at the base of Trinity-Wykeham Glaciers in response to variable surface melt rates between 2016 and 2019. GlaDS couples distributed and channelized subglacial drainage, allowing the mathematical model to capture the spatiotemporal evolution of the subglacial drainage system. The interplay between these two modes of drainage is highly influential on glacier dynamics. Satellite-derived datasets are used as inputs to the model, including basal and surface topography, surface velocities, and daily ice surface runoff products. Model outputs including subglacial water sheet thickness, water pressure, and channel discharge are compared to satellite-derived glacier surface velocities to determine how subglacial hydrology affects Trinity-Wykeham Glaciers. Nine model runs are completed to gauge the sensitivity of the GlaDS model to variations in two poorly constrained parameters that control the ease of subglacial water flow and determine a practical range of values for these parameters in future studies. \n \nThe results of this project suggest that Trinity-Wykeham Glaciers’ velocities are directly influenced by surface melting rates and the configuration of the subglacial hydrology networks. Model outputs indicate that high subglacial water pressures cause acceleration at Trinity-Wykeham Glaciers and that with a sufficiently high influx of water to the bed, an efficient channelized drainage network develops that reduces local water pressures and causes a drop in flow velocity. The year 2018 is identified as a year in which channelized drainage is minimal, resulting in comparatively high water pressures and velocities after the melt season.

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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.015
Threshold uncertainty score0.096

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0020.001
Scholarly communication0.0020.001
Open science0.0020.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0050.000

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.

Opus teacher head0.017
GPT teacher head0.177
Teacher spread0.161 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2023
Admission routes1
Has abstractyes

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