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Record W4386495717 · doi:10.1029/2023gl104095

Closing Greenland's Mass Balance: Frontal Ablation of Every Greenlandic Glacier From 2000 to 2020

2023· article· en· W4386495717 on OpenAlexafffund
William Kochtitzky, Luke Copland, Michalea D. King, Romain Hugonnet, Hester Jiskoot, Mathieu Morlighem, Romain Millan, Shfaqat Abbas Khan, Brice Noël

Bibliographic record

VenueGeophysical Research Letters · 2023
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicCryospheric studies and observations
Canadian institutionsUniversity of LethbridgeUniversity of Ottawa
FundersNatural Sciences and Engineering Research Council of CanadaUniversity of Ottawa
KeywordsGlacier mass balanceGlacierGreenland ice sheetAblation zoneAblationGeologyClimatologyIce streamTidewater glacier cycleGlacier ice accumulationPhysical geographyOceanographyCryosphereGeomorphologySea iceGeographyMedicineIce calving

Abstract

fetched live from OpenAlex

Abstract In Greenland, 87% of the glacierized area terminates in the ocean, but mass lost at the ice‐ocean interface, or frontal ablation, has not yet been fully quantified. Using measurements and models we calculate frontal ablation of Greenland's 213 outlet and 537 peripheral glaciers and find a total frontal ablation of 481.8 ± 24.0 for 2000–2010 and 510.2 ± 18.6 Gt a−1 for 2010–2020. Ice discharge accounted for ∼90% of frontal ablation during both periods, while mass loss due to terminus retreat comprised the remainder. Only 16 glaciers were responsible for the majority (>50%) of frontal ablation from 2010 to 2020. These estimates, along with the climatic‐basal balance, allow for a more complete accounting of Greenland Ice Sheet and peripheral glacier mass balance. In total, Greenland accounted for ∼90% of Northern Hemisphere frontal ablation for 2000–2010 and 2010–2020.

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)0.000
metaresearch head score (Gemma)0.000
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 score0.070
Threshold uncertainty score0.139

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
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 head0.037
GPT teacher head0.285
Teacher spread0.247 · 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

Citations21
Published2023
Admission routes2
Has abstractyes

Explore more

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