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Record W2120334143 · doi:10.1029/2000gc000098

Late Pleistocene sea level variations derived from the Argentine Shelf

2000· article· en· W2120334143 on OpenAlexaff
T. P. Guilderson, Lloyd H. Burckle, Sidney R. Hemming, W. R. Peltier

Bibliographic record

VenueGeochemistry Geophysics Geosystems · 2000
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicGeology and Paleoclimatology Research
Canadian institutionsUniversity of Toronto
FundersLamont-Doherty Earth Observatory, Columbia UniversityLawrence Livermore National LaboratoryU.S. Department of Energy
KeywordsGeologySea levelPleistocenePost-glacial reboundIsostasyGlacial periodLithosphereLast Glacial MaximumOceanographySedimentary rockTectonicsPaleontologyClimatology

Abstract

fetched live from OpenAlex

Neritic‐littoral marine sedimentary deposits from the Argentine Shelf provide a record of late Pleistocene sea level variation. Sediments interpreted to reflect the last glacial maximum low‐stand are ∼150 m below present. Using a simple Airy isostatic model for hydroisostatic compensation and correcting for a minor tectonic component yields a local eustatic sea level lowering of ∼105 m at the last glacial maximum. The deglacial sea level curve records two rapid sea level rises consistent with MWP1‐A and 1‐B as documented by the Barbados coral‐based sea level curve. Comparison with relative sea level variations predicted by the ICE4G VM2 viscoelastic model have highlighted a deficiency in the model's predicted sea level history for this region. Detailed data‐model comparisons of late Pleistocene sea level variations are necessary in the face of climate change induced sea level perturbations to determine regional and or systematic biases in the treatment of lithosphere viscosity and accurate predictions of future sea level.

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 score0.072
Threshold uncertainty score0.143

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 communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.022
GPT teacher head0.220
Teacher spread0.198 · 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

Citations139
Published2000
Admission routes1
Has abstractyes

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