Change in equilibrium-line altitude (ΔELA) for 215 Alaskan glaciers from the Little Ice Age (LIA) maximum to present (2016 to 2024)
Bibliographic record
Abstract
The study associated with this dataset evaluates the climatic signature of the Little Ice Age (LIA; ~1250 to 1900) relative to the present (2016–2024) by reconstructing shifts in equilibrium-line altitude (ΔELA), a climate-sensitive parameter, for 215 glaciers in Alaska and adjacent Canada (southwest Yukon and northwest British Columbia) using remote sensing and a suite of geographic information system (GIS) tools. We quantify the magnitude and spatial pattern of ΔELA across Alaska’s major glaciated regions to infer centennial-scale changes in temperature and precipitation and to examine links with synoptic-scale circulation associated with the Aleutian Low. Data were generated in 2024–2025 at Arizona State University and Northern Arizona University as part of the NSF-funded PROGLACIAL project. The dataset includes reconstructed LIA maximum ELAs, modern end-of-summer snowline altitudes (2016–2024), and LIA–present ΔELA for 215 glaciers. LIA glacier extents were compiled from published sources and additional mapping of geomorphic evidence (terminal moraines, trimlines) using the ESRI World Imagery basemap; LIA glacier surfaces and ELAs were modeled using the GlaRe and PalaeoIce GIS toolboxes; and modern snowline altitudes were manually mapped in Sentinel-2 satellite imagery (2016-2024) and associated elevation extracted from the ArcticDEM (10 meter mosaic product).
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".