Glacier Distributions and Climate in the Canadian Rockies
Bibliographic record
Abstract
Glacier-climate relationships in the Canadian Rockies have been documented previously through mass-balance studies of individual glaciers and local meteorological parameters. In terms of regional significance, however, the relationship between the areal distribution of glaciers and regional climate is perhaps more important in evaluating large-scale responses to climate forcings. The purpose of the current study is to establish which climate variables are responsible for the observed distribution of glaciers. Using a 1:50,000 digitized coverage of glaciers in the Canadian Rockies, a 1-km resolution Digital Elevation Model (DEM) and climate normals from 88 stations throughout the study area, the authors examine the correlation between climate variables and the distribution of glacial ice in the Canadian Rockies. Through the construction of climatic lapse rates, sea-surface interpolation, and subsequent extrapolation based on the DEM, simple cell climatologies that reflect both the altitudinal and regional variations in temperature and precipitation are developed for the study area. Normalized Ice Coverage values prescribed for the study cells from the digitized coverage are then examined through a statistical framework which suggests that spring precipitation, annual temperatures, and winter precipitation are the strongest predictors of glacier distributions in the Canadian Rockies.
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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 | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".