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Record W3035824700 · doi:10.1029/2020gl088796

Elevation Changes of the Fennoscandian Ice Sheet Interior During the Last Deglaciation

2020· article· en· W3035824700 on OpenAlexaff
Timothy Lane, Øyvind Paasche, Bjørn Christian Kvisvik, Kathryn Adamson, Ángel Rodés, Henry Patton, Natalya Gomez, Delia M. Gheorghiu, Jostein Bakke, Alun Hubbard

Bibliographic record

VenueGeophysical Research Letters · 2020
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicGeology and Paleoclimatology Research
Canadian institutionsMcGill University
FundersNatural Environment Research CouncilNorges ForskningsrådSight Research UK
KeywordsDeglaciationGeologyIce sheetYounger DryasAntarctic ice sheetMoraineIce-sheet modelPhysical geographyCryosphereThinningGlaciologyOceanographyIce streamGeomorphologyGlacierSea iceHolocenePaleontologyGeographyVolcanism

Abstract

fetched live from OpenAlex

Abstract The dynamics and paleo‐glaciology of ice sheet interiors during the last deglaciation are poorly constrained, hindering ice sheet model reconstructions. We provide direct evidence of Fennoscandian Ice Sheet (FIS) interior behavior during deglaciation through surface exposure dating. Our results demonstrate early thinning of the FIS, prior to the Younger Dryas (YD, 12.8–11.7 ka). Interior thinning in central Norway was concurrent with retreat along the coastline, exposing ice‐free mountainous tracts, potentially as early as 20–15 ka. The FIS then formed moraines in these ice‐free tracts during the YD. This is contrary to current hypotheses advocating a landscape fully covered by cold, inactive ice during this period. Present empirical and model reconstructions fail to capture rapid interior downwastage, increasing uncertainties in ice sheet volume estimates and sea level contributions.

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)0.001
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 score0.105
Threshold uncertainty score0.209

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.034
GPT teacher head0.271
Teacher spread0.237 · 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

Citations27
Published2020
Admission routes1
Has abstractyes

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