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Record W2159225273 · doi:10.1029/2006gl027511

Mass balance of glaciers and ice caps: Consensus estimates for 1961–2004

2006· article· en· W2159225273 on OpenAlexaff
Georg Kaser, J. Graham Cogley, Mark B. Dyurgerov, M. F. Meier, A. Ohmura

Bibliographic record

VenueGeophysical Research Letters · 2006
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicCryospheric studies and observations
Canadian institutionsTrent University
Fundersnot available
KeywordsGlacierBalance (ability)Ice capsIce sheetPhysical geographyGeologyClimatologyGlacier mass balanceSea level riseEnvironmental scienceGeographyOceanographyClimate changeMedicine

Abstract

fetched live from OpenAlex

Working with comprehensive collections of directly‐measured data on the annual mass balance of glaciers other than the two ice sheets, we combine independent analyses to show that there is broad agreement on the evolution of global mass balance since 1960. Mass balance was slightly below zero around 1970 and has been growing more negative since then. Excluding peripheral ice bodies in Greenland and Antarctica, global average specific balance for 1961–1990 was −219 ± 112 kg m −2 a −1 , representing 0.33 ± 0.17 mm SLE (sea‐level equivalent) a −1 . For 2001–2004, the figures are −510 ± 101 kg m −2 a −1 and 0.77±0.15 mm SLE a −1 . Including the smaller Greenland and Antarctic glaciers, global total balance becomes 0.38 ± 0.19 mm SLE a −1 for 1961–1990 and 0.98 ± 0.19 mm SLE a −1 for 2001–2004. For 1991–2004 the glacier contribution, 0.77 ± 0.26 mm SLE a −1 , is 20–30% of a recent estimate of 3.2 ± 0.4 mm a −1 of total sea‐level rise for 1993–2005. While our error estimates are not rigorous, we believe them to be liberal as far as they go, but we also discuss several unquantified biases of which any may prove to be significant.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.059
Threshold uncertainty score0.118

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.004
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0070.006
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.001

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.

Opus teacher head0.034
GPT teacher head0.286
Teacher spread0.252 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations492
Published2006
Admission routes1
Has abstractyes

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