Glacier Water Resources on the Eastern Slopes of the Canadian Rocky Mountains
Bibliographic record
Abstract
Maps of glacier area in western Canada have recently been generated for 1985 and 2005 (Bolch et al., 2010), providing the first complete inventory of glacier cover in Alberta and British Columbia. Western Canada lost about 11% of its glacier area over this period, with area loss exceeding 20% on the eastern slopes of the Canadian Rockies. Glacier area is difficult to relate to glacier volume, which is the attribute of relevance to water resources and global sea level rise. We apply several possible volume-area scaling relations and glacier slope-thickness relations to estimate the volume of glacier ice in the headwater regions of rivers that spring from the eastern slopes of the Canadian Rocky Mountains, arriving at an estimate of 55 ± 15 km3. We cannot preclude higher values, because the available data indicate that large valley glaciers in the Rocky Mountains may be anomalously thick relative to what is typical in the global database that forms the basis for empirical volume-area scaling relations. Incorporating multivariate statistical analysis using observed mass balance data from Peyto Glacier, Alberta and synoptic meteorological conditions in the Canadian Rockies (1966–2007), we model future glacier mass balance scenarios on the eastern slopes of the Rockies. We simulate future volume changes for the glaciers of the Rockies by using these mass balance scenarios in conjunction with a regional ice dynamics model. These projections indicate that glaciers on the eastern slopes will lose 80–90% of their volume by 2100. Glacier contributions to streamflow in Alberta decline from 1.1 km3 a−1 in the early 2000s to 0.1 km3 a−1 by the end of this century.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 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) | 0.005 | 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".