Huss Receives 2013 Cryosphere Young Investigator Award: Citation
Bibliographic record
Abstract
Matthias Huss is an outstanding young glaciologist who completed his Ph.D. in 2009 at the Swiss Federal Institute of Technology (ETH) in Zurich, Switzerland, and currently has a joint appointment there and at the University of Fribourg. During his short scientific career, Matthias has made substantial and innovative contributions to a broad range of topics in glaciology, including modeling and projection of glacier mass and runoff changes, glacier outburst floods, glacier dynamics, and climate‐glacier interactions. His work ranges from local‐scale to regional‐ and global‐scale studies. Of note, he developed a new cutting‐edge and already widely used parameterization to model glacier retreat, which fills the gap between complex ice flow models and simple scaling methods. He also computed ice thickness distribution maps of all 200,000 glaciers in the world based on principles of flow dynamics. This unique data set has many applications, of which perhaps the most significant is the estimation of the total volume of stored ice, a quantity that is critical for projections of sea level rise and water resources during the 21st century.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.002 | 0.001 |
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".