Glaciers of the Olympic Mountains, Washington—The Past and Future 100 Years
Bibliographic record
Abstract
Abstract In 2015, the Olympic Mountains contained 255 glaciers and perennial snowfields totaling 25.34 ± 0.27 km 2 , half of the area in 1900, and about 0.75 ± 0.19 km 3 of ice. Since 1980, glaciers shrank at a rate of −0.59 km 2 yr −1 during which time 35 glaciers and 16 perennial snowfields disappeared. Area changes of Blue Glacier, the largest glacier in the study region, was a good proxy for glacier change of the entire region. Modeled glacier mass balance, based on monthly air temperature and precipitation, correlates with glacier area change. The mass balance is highly sensitive to changes in air temperature rather than precipitation, typical of maritime glaciers. In addition to increasing summer melt, warmer winter temperatures changed the phase of precipitation from snow to rain, reducing snow accumulation. Changes in glacier mass balance are highly correlated with the Pacific North American index, a proxy for atmospheric circulation patterns and controls air temperatures along the Pacific Coast of North America. Regime shifts of sea surface temperatures in the North Pacific, reflected in the Pacific Decadal Oscillation (PDO), trigger shifts in the trend of glacier mass balance. Negative (“cool”) phases of the PDO are associated with glacier stability or slight mass gain whereas positive (“warm”) phases are associated with mass loss and glacier retreat. Over the past century the overall retreat is due to warming air temperatures, +0.7°C in winter and +0.3°C in summer. The glaciers in the Olympic Mountains are expected to largely disappear by 2070.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 teacher head, 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".