Significant contribution to total mass from very small glaciers
Bibliographic record
Abstract
Abstract. A single large glacier can contain tens of millions of times the mass of a small glacier. Nevertheless, very small glaciers (with area ≤1 km2) are so numerous that their contribution to the world's total ice volume is significant and may be a notable source of error if excluded. With current glacier inventories, total global volume errors on the order of 10% are possible. However, to reduce errors to below 1% requires the inclusion of glaciers that are smaller than those recorded in most inventories. At the global scale, 1% accuracy requires a list of all glaciers and ice caps (GIC, exclusive of the ice sheets) larger than 1 km2, and for regional estimates requires a complete list of all glaciers down to the smallest possible size. For this reason, sea-level rise estimates and other total mass and total volume analyses should not omit the world's smallest glaciers. In particular, upscaling GIC inventories has been common practice in sea level estimates, but downscaling may also be necessary to include the smallest glaciers.
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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) | 0.004 | 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; both teacher heads agree on what is shown here.
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".