Bias-corrected estimates of glacier thickness in the Columbia River Basin, Canada
Bibliographic record
Abstract
Abstract Several global datasets of glacier thickness exist, but the number of observations from western Canada are sparse and spatially biased. To supplement these limited observations, we measured ice thickness with ice penetrating radar on five glaciers in the Columbia Mountains, Canada. Our radar surveys, when combined with previous surveys for two glaciers in the Rocky Mountains, total 182 km of transects that represent 34 672 point measurements of ice thickness. Our measurements are, on average, 38% thicker than previous surface inversion model estimates of glacier thickness. Using our measurements within a cross-validation scheme, we model ice thickness with the Open Global Glacier Model (OGGM) driven with recent observations of surface mass balance and glacier elevation. We calibrated OGGM ice thickness by optimizing the ice creep parameter in the model. The optimized OGGM yields an ice volume for Columbia Basin of 122.5 ± 22.4 km3for the year 2000, which is 23% greater than the range of previous estimates. At current rates of glacier mass loss for this region, glaciers would disappear from the basin in about 65–80 years. Disappearance of these glaciers will negatively affect the basin's surface hydrology, freshwater availability and aquatic ecosystems.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".