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Record W2068437095 · doi:10.3189/172756410790595840

Completing the World Glacier Inventory

2009· article· en· W2068437095 on OpenAlexaboutno aff
Atsumu Ohmura

Bibliographic record

VenueAnnals of Glaciology · 2009
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicCryospheric studies and observations
Canadian institutionsnot available
Fundersnot available
KeywordsGlacierCryosphereGlacier mass balancePhysical geographyGeologyGlacier morphologyGlaciologyArcticClimatologyWater cycleSnowGeographyIce streamOceanographySea iceGeomorphology

Abstract

fetched live from OpenAlex

Abstract An inventory of the surface area and volume of the world’s glaciers, outside Greenland and Antarctica, was part of the International Hydrological Decade (1965–74). It was considered essential to an understanding of the role played by glaciers in the hydrological cycle and was to be repeated every 50 years to detect change. To date, 46% of the estimated total glacier area has been inventoried and made available through the World Glacier Monitoring Service and the US National Snow and Ice Data Center. As the original inventory method was too time-consuming and inapplicable for some areas, a simplified method was developed in the early 1980s using satellite images. The Global Land Ice Measurements from Space (GLIMS) project now covers 34% of the estimated glacierized area outside Greenland and Antarctica. Both inventory efforts have made good progress and contributed substantially to our knowledge of glaciology and its related sciences, but global coverage is still incomplete. If both inventories are combined, 46% of the world’s glacierized area is still missing; 26% is covered by both methods, which allows the quality of the satellite-based and semi-automatic inventories to be assessed by comparison. About 95 000 glaciers remain to be inventoried, of which about half are in the Canadian Cordillera, South America and the Canadian Arctic Islands. As the cryosphere is changing rapidly, it is of the utmost importance to complete the global glacier inventory as soon as possible, and identify an appropriate repeat cycle.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.132
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.098
GPT teacher head0.305
Teacher spread0.206 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations45
Published2009
Admission routes1
Has abstractyes

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