A data set of world-wide glacier length fluctuations
Bibliographic record
Abstract
Abstract. Glacier fluctuations contribute to variations in sea level and historical glacier length fluctuations are natural indicators of climate change. To study these subjects, long-term information of glacier change is needed. In this paper we present a~data set of global long-term glacier length fluctuations. The data set is a compilation of available information on changes in glacier length world-wide, including both measured and reconstructed glacier length fluctuations. All 471 length series start before 1950 and cover at least four decades. The longest record starts in 1534, but the majority of time series start after 1850. The number of available records decreases again after 1962. The data set has global coverage including records from all continents. However, the Canadian Arctic is not represented in the data set. The glacier length series show relatively small fluctuations until the mid-19th century followed by a global retreat that was strongest in the first half of the 20th century, although large variability in the length change of the different glaciers is observed. During the 20th century, calving glaciers retreated more than land terminating glaciers, but their relative length change was approximately equal. Besides calving, the glacier slope is the most important glacier property determining length change: steep glaciers have retreated less than glaciers with a gentle slope.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.007 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.003 |
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".