Bibliographic record
Abstract
Glaciers play an important role in the evolution of many mountain landscapes.The primary objective of this study was to model rates of contemporary glacier erosion through numerical modeling.The research uses a regional glaciation model and couples to it (i) a process-driven erosion model that includes abrasion and quarrying; and (ii) a sliding-based power relation erosion model.The coupled erosion models were then used to estimate erosion for Peyto Glacier.The models predict higher rates of erosion than those estimated from Peyto Lake sediments.Contrary to observed sediment yield, both models predict a decrease in erosion during glacial retreat.Changes in sediment storage are believed to account for the discrepancy seen.Over the past 2000 years, major differences exist between the models during times of abrupt climate change.These differences depend on the inter-decadal to century-scale variability inherent in the climate proxy data used to force the models.This thesis could not have been completed without the support of many people.Fore most, I would like to thank my supervisor, Dr. Brian Menounos, for his patience and support through my transition into the earth sciences, my period of over-teaching, and the thesis journey itself
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 | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".