An Intensive Observation of Calving at Helheim Glacier, East Greenland
Bibliographic record
Abstract
Calving of glacial ice into the ocean from the Greenland Ice Sheet is an important component of global sea level rise. The calving process itself is relatively poorly observed, understood, and modeled; as such, it represents a bottleneck in improving future global sea level estimates in climate models. We organized a pilot project to observe the calving process at Helheim Glacier in East Greenland in an effort to better understand it. During an intensive one-week survey, we deployed a suite of instrumentation including a terrestrial radar interferometer, GPS receivers, seismometers, tsunameters, and an automated weather station. This effort captured a calving process and measured various glaciological, oceanographic, and atmospheric parameters before, during, and after the event. One outcome of our observations is evidence that the calving process actually consists of a number of discrete events, spread out over time, in this instance over at least two days. This time span has implications for models of the process. Realistic projections of future global sea level will depend on accurate parametrization of calving, which will require more sustained observations.
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 | 0.000 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".