Detecting the Effects of Sustained Glacier Wastage on Streamflow in Variably Glacierized Catchments
Bibliographic record
Abstract
This study focused on the effects of glacier wastage on streamflow in the Canadian portion of the Columbia River headwaters over the period 1977 to 2017. Between 1985 and 2013, glacier coverage decreased by up to 2% of catchment area for the 35 study catchments. The mean wastage flux contribution to streamflow had a positive relation with fractional glacier coverage and an inverse relation with catchment water yield. Glacier mass change estimates suggest that wastage flux contributions declined between 1985-1999 and 2000-2018, but the estimates are subject to substantial uncertainty. Annual wastage flux contributions over a four-year period for two study catchments ranged from 8 to 13% of annual water yield for a catchment with 17% glacier cover, with glaciers extending below treeline, and 9 to 19% for a smaller alpine catchment with 57% glacier cover. After accounting statistically for climatic forcing and non-glacial contributions to streamflow, August runoff from glacierized catchments decreased through time at a rate that was linearly related to loss of glacier cover. The analyses suggest that glacier-melt contributions to August runoff have already have passed peak water, and that these reductions have exacerbated a regional climate-driven trend to decreased August streamflow contributions from unglacierized areas.
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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 | 0.004 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".