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Record W2082061235 · doi:10.1029/2011eo190004

Improving Hydrology in Land Ice Models

2011· article· en· W2082061235 on OpenAlexaff
Stephen Price, Gwenn E. Flowers, Christian Schoof

Bibliographic record

VenueEos · 2011
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicCryospheric studies and observations
Canadian institutionsUniversity of British ColumbiaSimon Fraser University
Fundersnot available
KeywordsCryosphereSea iceEnvironmental scienceIce sheetGlacierClimatologyClimate changeIce shelfClimate modelIce streamPhysical geographyHydrology (agriculture)GeologyGeographyOceanography

Abstract

fetched live from OpenAlex

Community Earth System Model Land Ice Working Group Meeting; Boulder, Colorado, 13 January 2011; Recent observations indicate that mass loss from glaciers and ice sheets (“land ice”) is increasing. The drivers of these changes are not well understood, and modeling the land ice response to them remains challenging. As a result, the Intergovernmental Panel on Climate Change explicitly avoided speculating on 21st‐century sea level rise from ice dynamical processes in its fourth assessment report. The mismatch between observations of land ice change and model skill at mimicking those changes is behind recent efforts to develop next‐generation land ice models. Necessary improvements to existing models include improved dynamics, coupling to climate models, and better representations of important boundary conditions and physical processes. Basal sliding, the primary control on the rate of land ice delivery to the oceans, is one such boundary condition that is largely controlled by land ice hydrology.

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)0.002
metaresearch head score (Gemma)0.008
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.035
Threshold uncertainty score0.069

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.008
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.003
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.001

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 head0.044
GPT teacher head0.196
Teacher spread0.152 · 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 designSimulation or modeling
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

Citations2
Published2011
Admission routes1
Has abstractyes

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