Recommendations for the compilation of glacier inventory data from digital sources
Bibliographic record
Abstract
Abstract Modern geoinformatic techniques allow the automated creation of detailed glacier inventory data from glacier outlines and digital terrain models (DTMs). Once glacier entities are defined and an appropriate DTM is available, several methods exist to derive the inventory data (e.g. minimum, maximum and mean elevation; mean slope and aspect) for each glacier from digital intersection of both datasets. Compared to the former manual methods, the new grid-based statistical calculations are very fast and reproducible. The major aim of this contribution is to help in standardizing the related calculations to enhance the integrity of the Global Land Ice Monitoring from Space (GLIMS) database. The recommendations were prepared by a working group and also contribute to the European Space Agency project GlobGlacier. The document follows the former UNESCO manual for the production of the World Glacier Inventory published in 1970, identifies the potential pitfalls, and describes the differences from the former methods of compilation. The online background material for this paper (see http://www.glims.org) contains example scripts for calculation of each parameter and will be updated when required.
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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 | 0.035 | 0.136 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.019 | 0.018 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.009 |
| Open science | 0.006 | 0.004 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.032 | 0.044 |
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".