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Record W4415778988 · doi:10.1017/jog.2025.10092

Ice sheet mass flow and balance with constituent terms 2010–19

2025· article· en· W4415778988 on OpenAlexaff
Kenneth D. Mankoff, Chad A. Greene, Alex Gardner, Benjamin Davison, Désirée Treichler, William Kochtitzky, Brice Van Liefferinge, Chang‐Qing Ke, Thorben Döhne, Robert S. Fausto, Damien Ringeisen

Bibliographic record

VenueJournal of Glaciology · 2025
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicCryospheric studies and observations
Canadian institutionsEnvironment and Climate Change Canada
FundersCalifornia Institute of TechnologyJet Propulsion LaboratoryNational Aeronautics and Space Administration
KeywordsIce streamIce sheetIce-sheet modelGreenland ice sheetFuture sea levelAntarctic ice sheetIce shelfAntarctic sea iceSea iceCryosphere

Abstract

fetched live from OpenAlex

Abstract Ice sheet mass loss is typically provided for grounded ice, because changes in floating ice are more difficult to measure and contribute minimally to sea level rise. However, gross freshwater mass flow rate across ice sheet boundaries, including floating ice, is a better metric of ice sheet health. Here, we present total mass flows across ice sheet boundaries for both the Greenlandic and Antarctic ice sheets and their peripheral glaciers from 2010 through 2019. In addition to total mass flow, we provide constituent terms and gross rather than net values, including components that combine to provide surface mass balance. Ice mass loss in Greenland is 330 $\pm$ 50 Gt yr -1 which is $\sim$ 30% larger than the 255 $\pm$ 40 Gt yr -1 grounded ice mass loss estimates that neglect floating ice changes. Ice mass loss in Antarctica is 450 $\pm$ 270 Gt yr -1 which is $\sim$ 2.4x the 190 $\pm$ 115 Gt yr -1 grounded ice mass loss estimates. Freshwater mass flow rate from Greenland is $\sim$ 1065 $\pm$ 120 Gt yr -1 or $\sim$ 3x mass loss ( $\sim$ 4x grounded mass loss), and from Antarctica is $\sim$ 3110 $\pm$ 1515 Gt yr -1 , or $\sim$ 7x mass loss ( $\sim$ 16x grounded mass loss).

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.044
Threshold uncertainty score0.326

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)0.0000.000

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 head0.011
GPT teacher head0.217
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.

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

Citations0
Published2025
Admission routes1
Has abstractyes

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