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Record W2047400748 · doi:10.1016/j.yqres.2014.04.002

A relative sea-level history for Arviat, Nunavut, and implications for Laurentide Ice Sheet thickness west of Hudson Bay

2014· article· en· W2047400748 on OpenAlexafffundabout
Karen M. Simon, T. S. James, Donald L. Forbes, Alice M. Telka, Arthur S. Dyke, J. A. Henton

Bibliographic record

VenueQuaternary Research · 2014
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicGeology and Paleoclimatology Research
Canadian institutionsMemorial University of NewfoundlandUniversity of VictoriaGeological Survey of CanadaNatural Resources Canada
FundersUniversity of California, IrvineArcticNetUniversity of Victoria
KeywordsGeologyPost-glacial reboundDeglaciationIce sheetHoloceneSea levelBayRadiocarbon datingLast Glacial MaximumOceanographyElevation (ballistics)Glacial periodTectonic upliftPhysical geographyPaleontologyTectonicsGeography

Abstract

fetched live from OpenAlex

Abstract Thirty-six new and previously published radiocarbon dates constrain the relative sea-level history of Arviat on the west coast of Hudson Bay. As a result of glacial isostatic adjustment (GIA) following deglaciation, sea level fell rapidly from a high-stand of nearly 170 m elevation just after 8000 cal yr BP to 60 m elevation by the mid Holocene (~ 5200 cal yr BP). The rate of sea-level fall decreased in the mid and late Holocene, with sea level falling 30 m since 3000 cal yr BP. Several late Holocene sea-level measurements are interpreted to originate from the upper end of the tidal range and place tight constraints on sea level. A preliminary measurement of present-day vertical land motion obtained by repeat Global Positioning System (GPS) occupations indicates ongoing crustal uplift at Arviat of 9.3 ± 1.5 mm/yr, in close agreement with the crustal uplift rate inferred from the inferred sea-level curve. Predictions of numerical GIA models indicate that the new sea-level curve is best fit by a Laurentide Ice Sheet reconstruction with a last glacial maximum peak thickness of ~ 3.4 km. This is a 30–35% thickness reduction of the ICE-5G ice-sheet history west of Hudson Bay.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation 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 score0.056
Threshold uncertainty score0.507

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.125
GPT teacher head0.346
Teacher spread0.221 · 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 teacher head, 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

Citations22
Published2014
Admission routes3
Has abstractyes

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