Glacier status and contribution to streamflow in the Olympic Mountains, Washington, USA
Bibliographic record
Abstract
Abstract The Olympic Peninsula, Washington, USA, currently holds 184 alpine glaciers larger than 0.01 km2 and their combined area is 30.2 ± 0.95 km2. Only four glaciers are >1 km2 and 120 of the others are <0.1 km2. This represents a loss of 82 glaciers and a 34% decrease in combined area since 1980, with the most pronounced losses occurring on south-facing aspects and in the more arid northeastern part of the range. Annual rate of loss in glacier area for seven of the largest glaciers accelerated from 0.26 km2 a−1 (1900–80) to 0.54 km2 a−1 (1980–2009). Thinning rates on four of the largest glaciers averaged nearly 1 ma−1 from 1987 to 2010, resulting in estimated volume losses of 17–24%. Combined glacial snow, firn and ice melt in the Hoh watershed is in the range 63–79 ± 7 × 106 m3, or 9–15% of total May–September streamflow. In the critical August–September period, the glacial fraction of total basin runoff increases to 18–30%, with one-third of the water directly from glacial ice (i.e. not snow and firn). Glaciers in the Elwha basin produce 12–15 ± 1.3 × 106 m3 (2.5–4.0%), while those in the Dungeness basin contribute 2.5–3.1 ± 0.28 × 106 m3 (3.0–3.8%).
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".