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Record W2888771143 · doi:10.1029/2018gl078568

Changes in Deep Water Oxygenation of the South China Sea Since the Last Glacial Period

2018· article· en· W2888771143 on OpenAlexaff
Gang Li, Harunur Rashid, L. Zhong, Xing Xu, Wen Yan, Zhong Chen

Bibliographic record

VenueGeophysical Research Letters · 2018
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicGeology and Paleoclimatology Research
Canadian institutionsMemorial University of Newfoundland
FundersYouth Innovation Promotion Association of the Chinese Academy of SciencesChinese Academy of SciencesNatural Science Foundation of Guangdong ProvinceNational Natural Science Foundation of China
KeywordsOceanographyGeologyLast Glacial MaximumNorth Atlantic Deep WaterDeep seaGlacial periodPeriod (music)Benthic zoneCircumpolar deep waterDeep waterStadialClimatologyHolocenePaleontology

Abstract

fetched live from OpenAlex

Abstract The Pacific meridional overturning circulation is thought to have a significant influence on global climate. However, the extent to which intermediate and deep circulations have changed in the Pacific Ocean since the Last Glacial Maximum (LGM) is not well known. At present, the South China Sea Deep Water (SCSDW) is fed by the upper Pacific Deep Water. Here we present new benthic foraminiferal δ13C and redox‐sensitive elemental data from a sediment core retrieved from the southern deep SCS to reconstruct the oxygenation history of the SCSDW since the LGM. Oxygenation records from the deep SCS and intermediate and deep waters in the Pacific Ocean demonstrate that the SCSDW deeper than 1,600 m has been sourced by the Pacific Deep Water since the LGM. Our data suggest that the well‐ventilated North Pacific Intermediate Water would not have influenced the SCSDW during cold stadials of the last deglacial period.

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 score0.033
Threshold uncertainty score0.066

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 communication0.0000.000
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 head0.023
GPT teacher head0.270
Teacher spread0.246 · 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

Citations45
Published2018
Admission routes1
Has abstractyes

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