An approach to remotely monitor glacier mass balance at seasonal to annual time scales, Columbia and Rocky Mountains, Canada.
Bibliographic record
Abstract
My dissertation investigates glacier mass change in the Columbia and Rocky Mountains of British Columbia. In chapter one I discuss the importance of the cryosphere and glaciers, introduce the climate and glaciers of the study region, and outline the objectives and structure of this dissertation. Previous work established the feasibility of geodetic methods to accurately produce winter glacier mass balance and annual glacier mass balance. These studies demonstrate that geodetic surveys can be used to estimate mass balance during the accumulation season or for one glacier over a number of years. In chapter two, I refine these published methods to measure seasonal and annual mass balance for six glaciers within two mountain ranges from 2014–2018. I use synchronous field-based glaciological measurements, airbornelaser scanningsurveys (ALS) and satelliteimagery to quantify seasonal glacier mass change from 2014–2018. Chapter three reports on radar surveys I completed of the study glaciers, adding important observations to the global database of ice thickness. I use these observations and an existing flowline model, driven with observations of surface mass balance and glacier elevation to bias-correct ice thickness estimates for each glacier. Finally, I use the model to estimate ice thickness for all glaciers in the Columbia Basin and estimate total ice volume. Chapter four builds upon previous work which used surface topography, glacier mass balance, ice thickness, and ice velocity data to estimate ice flux at discrete glacier cross-sections. Previous efforts to infer the spatial distribution of mass balance have focused on glacier tongues. I expand upon this method, calculating surface mass balance between flux gates over the entire elevation range of three glaciers, over three years. I derive the altitude-mass balance relation and demonstrate that the relation can be accurately described with high-resolution elevation and ice flux data, and suggest that this method can be expanded for large-scale estimates. Chapter five summarizes the study’s major findings, highlights its limitations and discussed its broader implications. Finally, I make recommendations that will address knowledge gaps, and improve our understanding of changing glacier conditions and ability to model glacier dynamics.
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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 | 0.003 | 0.004 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".