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Record W1946317024 · doi:10.1002/2015jf003580

Reconciling the ICE‐6G_C reconstruction of glacial chronology with ice sheet dynamics: The cases of Greenland and Antarctica

2015· article· en· W1946317024 on OpenAlexafffund
G.R. Stuhne, W. R. Peltier

Bibliographic record

VenueJournal of Geophysical Research Earth Surface · 2015
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicGeology and Paleoclimatology Research
Canadian institutionsUniversity of Toronto
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsIce sheetIce-sheet modelGlacial periodGeologyIce streamIce coreLast Glacial MaximumPhysical geographyClimatologyPaleontologyCryosphereGeomorphologySea iceGeography

Abstract

fetched live from OpenAlex

Abstract We describe a theoretical and numerical framework that has been developed to investigate the compatibility of the ICE‐6G_C reconstruction of the glaciation histories of the Greenland and Antarctic ice sheets with the latest understanding of ice physics. The ICE‐6G_C reconstruction has been produced solely on the basis of the theory of the glacial isostatic adjustment (GIA) process, and it has remained an issue as to whether such reconstructions of the time‐dependent thickness variations of grounded continental ice sheets were compatible with physics‐based ice mechanical considerations. Our analyses focus on the evolution over the last glacial cycle of these extent ice sheet complexes and demonstrate that the GIA‐inferred models are entirely consistent with such considerations when uncertainties in (net) mass balance history are taken fully into account.

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)0.007
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: Empirical
Teacher disagreement score0.098
Threshold uncertainty score0.195

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0010.002
Scholarly communication0.0020.001
Open science0.0010.001
Research integrity0.0010.000
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 head0.053
GPT teacher head0.302
Teacher spread0.249 · 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

Citations57
Published2015
Admission routes2
Has abstractyes

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