The slow advance of a calving glacier: Hubbard Glacier, Alaska, U.S.A.
Bibliographic record
Abstract
Abstract Hubbard Glacier is the largest tidewater glacier in North America. In contrast to most glaciers in Alaska and northwestern Canada, Hubbard Glacier thickened and advanced during the 20th century. This a typical behavior is an important example of how insensitive to climate a glacier can become during parts of the calving glacier cycle. As this glacier continues to advance, it will close the seaward entrance to 50 km long Russell Fjord and create a glacier-dammed, brackish-water lake. This paper describes measured changes in ice thickness, ice speed, terminus advance and fjord bathymetry of Hubbard Glacier, as determined from airborne laser altimetry, aerial photogrammetry, satellite imagery and bathymetric measurements.The data show that the lower regions of the glacier have thickened by as much as 83 m in the last 41 years, while the entire glacier increased in volume by 14.1 km 3. Ice speeds are generally decreasing near the calving face from a high of 16.5 md −1in1948 to11.5 md −1in 2001. The calving terminus advanced at an average rate of about 16 m a−1 between 1895 and 1948 and accelerated to 32 m a−1 since 1948. However, since 1986, the advance of the part of the terminus in Disenchantment Bay has slowed to 28 m a−1. Bathymetric data from the lee slope of the submarine terminal moraine show that between 1978 and 1999 the moraine advanced at an average rate of 32 m a−1, which is the same as that of the calving face.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".