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Record W2006396501 · doi:10.1002/jqs.2582

Influences of extratropical water masses on equatorial Pacific cold tongue variability during the past 160 ka as revealed by faunal evidence of planktic foraminifers

2012· article· en· W2006396501 on OpenAlexaff
Pai‐Sen Yu, Markus Kienast, Min‐Te Chen, Isabel Cacho, Jos Abel Flores, Mahyar Mohtadi, Alan C Mix

Bibliographic record

VenueJournal of Quaternary Science · 2012
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicGeology and Paleoclimatology Research
Canadian institutionsDalhousie University
Fundersnot available
KeywordsGeologyOceanographyGlacial periodUpwellingWater massLast Glacial MaximumTransectClimatologyPaleontologyHolocene

Abstract

fetched live from OpenAlex

Abstract Glacial cooling (∼1–5°C) in the eastern equatorial Pacific (EEP) cold tongue is often attributed to increased equatorial upwelling, stronger advection from the Peru–Chile Current (PCC), and to the more remote subpolar southeastern Pacific water mass. However, evidence is scarce for identifying unambiguously which process plays a more important role in driving the large glacial cooling in the EEP. To address this question, here we adopt a faunal calibration approach using planktic foraminifers with a new compilation of coretop data from the eastern Pacific, and present new downcore variation data of fauna assemblage and estimated sea surface temperatures (SSTs) for the past 160 ka (Marine Isotope Stage (MIS) 6) from ODP Site 1240 in the EEP. With significant improvement achieved by adding more coretop data from the eastern boundary current, our downcore calibration results indicate that most of the glacial cooling episodes over the past 160 ka in the EEP are attributable to increased influence from the subpolar water mass from high latitudes of the southern Pacific. By applying this new calibration of the fauna SST transfer function to a latitudinal transect of eastern Pacific (EP) cores, we find that the subpolar water mass has been a major dynamic contributor to EEP cold tongue cooling since MIS 6. Copyright © 2012 John Wiley & Sons, Ltd.

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)0.005
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 score0.005
Threshold uncertainty score0.668

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.002
Scholarly communication0.0000.001
Open science0.0010.000
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 head0.028
GPT teacher head0.289
Teacher spread0.261 · 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

Citations10
Published2012
Admission routes1
Has abstractyes

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