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Record W2117541961 · doi:10.1029/2006gl027261

Abrupt climatic events during the last glacial‐interglacial transition in Alaska

2006· article· en· W2117541961 on OpenAlexafffund
Feng Sheng Hu, David M. Nelson, G Clarke, Kathleen M. Rühland, Yongsong Huang, Darrell S. Kaufman, John P. Smol

Bibliographic record

VenueGeophysical Research Letters · 2006
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicGeology and Paleoclimatology Research
Canadian institutionsQueen's University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsStadialInterglacialGeologyGlacial periodYounger DryasPaleoclimatologyIce coreClimatologyOceanographyHoloceneBorealPhysical geographyAbrupt climate changePeriod (music)Climate changeGlobal warmingPaleontologyGeographyEffects of global warming

Abstract

fetched live from OpenAlex

Evidence is mounting that abrupt climatic shifts occurred during the last glacial‐interglacial transition (LGIT) in the North Atlantic and other regions. However, few high‐resolution climatic records of the LGIT exist from the high latitudes of the North Pacific rim. We analyzed lake sediments from southwestern Alaska for biogenic silica, organic carbon, organic nitrogen, diatom assemblages, and compound‐specific hydrogen isotopes. Results reveal climatic changes coincident with the Younger Dryas, Intra‐Allerød Cold Period, and Pre‐Boreal Oscillation. However, major discrepancies exist in the paleoclimate patterns of the Bølling‐Allerød interstadial between our data and the GISP2 δ18O record from Greenland, and causes are uncertain. These data suggest that the North Pacific and North Atlantic experienced similar reversals during climatic warming of the LGIT but that the Bølling‐Allerød cooling trend in the GISP2 δ18O record is probably not a hemispheric or global pattern.

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 score0.021
Threshold uncertainty score0.042

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 studies0.0010.000
Scholarly communication0.0010.000
Open science0.0000.001
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 head0.017
GPT teacher head0.274
Teacher spread0.257 · 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

Citations33
Published2006
Admission routes2
Has abstractyes

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