Bibliographic record
Abstract
Abstract A survey of available mass-balance data shows that glaciers on arctic islands, i.e. mountain glaciers and ice caps in northern Canada, Greenland, Svalbard and the Eurasian islands, share mass-balance characteristics of low annual amplitude and small interannual variability. By contrast, glaciers around the Arctic (e.g. in Alaska, Iceland, mainland Scandinavia and northern Eurasia) can have exceptionally large annual amplitude and interannual variability but otherwise share characteristics with glaciers in lower latitudes. The arctic island glaciers occur in areas with low annual precipitation and high annual temperature variability, i.e. in dry-cold or continental regions. Most glaciers surrounding the Arctic (Alaska, Iceland and Scandinavia) occur in areas with high annual precipitation and low annual temperature variability, i.e. in wet-warm or maritime regions. Earlier mass-balance modelling showed that arctic island glaciers have low sensitivity to temperature changes consistent with their low mass-balance amplitude. However, very large changes in mass balance could occur on arctic island glaciers if the sea ice surrounding the arctic islands were reduced so that the climate of the arctic islands becomes more maritime.
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 | 0.001 | 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 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".