The Randolph Glacier Inventory (RGI) version 7
Bibliographic record
Abstract
The Randolph Glacier Inventory (RGI) is a globally complete collection of digital glacier outlines, excluding the two ice sheets. It has become a pillar of glaciological research at global and regional scales for estimates of recent and future glacier changes, glacier mass balance, glacier contribution to sea-level rise, among others. The latest RGI version (V6) was released in July 2017. Here, we present a new version of the RGI (version 7.0), which is our best estimate of global glacier outlines around the year 2000. Unlike previous versions which were compiled by an ad-hoc manual process using different sources, RGI7.0 is generated directly from the Global Land Ice Measurements from Space (GLIMS) glacier database, ensuring full traceability of single outlines to their original authors. The dataset is generated automatically with Python scripts parsing the GLIMS database and selecting outlines according to community decisions (based on data availability, quality and closeness to the year 2000). Prior to its release, the dataset was available for open review from the scientific community, and further refined as necessary. About 70% of the outlines (30% of the total area) in RGI7.0 are obtained from new inventories that were submitted to GLIMS since the last release of RGI6.0 by different groups around the world. This led to considerable quality improvements especially in High Mountain Asia, Northern Canada, northern Greenland, Caucasus and Middle East, South America and New Zealand. RGI7.0 includes updated topographical and geometrical glacier attributes generated with a new community software. The new RGI generation process is open-source, fully reproducible and easily adaptable, making future updates straightforward to generate.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.041 | 0.062 |
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".