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Record W2049532521 · doi:10.1186/bf03352325

The influence of 5000 year-old and younger glacial mass variability on present-day crustal rebound in the Antarctic Peninsula

2014· article· en· W2049532521 on OpenAlexaff
Erik R. Ivins, C. A. Raymond, T. S. James

Bibliographic record

VenueEarth Planets and Space · 2014
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicGeology and Paleoclimatology Research
Canadian institutionsGeological Survey of Canada
FundersCalifornia Institute of TechnologyJet Propulsion LaboratoryNational Aeronautics and Space Administration
KeywordsGeologyIce sheetGlacial periodPeninsulaPost-glacial reboundDrawdown (hydrology)Last Glacial MaximumSea levelHolocenePhysical geographyClimatologyPresent dayOceanographyPaleontologyGeographyGroundwaterArchaeology

Abstract

fetched live from OpenAlex

Assessment of Antarctic rebound is complicated by two issues: (1) The total ice volume at Last Glacial Maximum is contentious, with estimates ranging from just a few meters to several tens of meters of equivalent eustatic sea level rise. (2) The late-Holocene mass budget is also uncertain. Space-based geodesy may provide important data in the coming years for estimating the recent ice mass balance state of Antarctica. Toward this end, GPS has an important role for isolating the solid earth movements that are associated with postglacial rebound. Here we provide numerical examples of vertical motions that are predicted by coupling realistic glacial load histories to 20th century ice mass imbalance estimates for the Antarctic Peninsula. The main complexity revealed by these examples is the striking difference among predictions that have an oscillatory mass change during the last 5000 to 50 years, as opposed to those having a continuous (non-oscillatory) mass drawdown of the grounded ice sheet.

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)0.001
metaresearch head score (Gemma)0.003
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.040
Threshold uncertainty score0.080

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.009
GPT teacher head0.221
Teacher spread0.212 · 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

Citations42
Published2014
Admission routes1
Has abstractyes

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