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Record W4402343095 · doi:10.1038/s43247-024-01668-1

The Yangtze River Delta experienced strong seasonality and regular summer upwelling during the warm mid-Holocene

2024· article· en· W4402343095 on OpenAlexaff
Matthias Alberti, Sandro F. Veiga, Bo Chen, Liang Hu, Zheng Fang, Baochun Zhou, Yanhong Pan

Bibliographic record

VenueCommunications Earth & Environment · 2024
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicGeology and Paleoclimatology Research
Canadian institutionsMinistry of Education and Child Care
FundersState Key Laboratory of Palaeobiology and StratigraphyNanjing UniversityFundamental Research Funds for the Central UniversitiesChinese Academy of SciencesNational Natural Science Foundation of China
KeywordsUpwellingSeasonalityHoloceneDeltaYangtze riverOceanographyClimatologyGeologyEnvironmental scienceGeographyChinaArchaeologyEcology

Abstract

fetched live from OpenAlex

Abstract The mid-Holocene climate optimum saw warm temperatures in large parts of China, but its impact on seasonal environmental changes is not fully understood yet. Here, we use high-resolution geochemical analyses of 7000 to 6000 year-old oyster shells from the Yangtze River Delta to reconstruct climatic and oceanographic patterns. The stable isotope (δ 18 O, δ 13 C) and clumped isotope data reflect prominent seasonal changes in temperature, precipitation, and river discharge. Summer months experienced warm temperatures and a distinct increase in rainfalls and river discharge. In contrast, winter months were characterized by a dry season, which might have been longer than today. Stable isotope data also indicate regular summer upwelling in the study area. These results partly disagree with available climate models raising doubts on the models’ reliability. Thus, our palaeo-proxy data offers the possibility to evaluate and correct climate models and thereby improve predictions for the future considering on-going global warming.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.058
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0020.002
Scholarly communication0.0000.000
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 head0.034
GPT teacher head0.262
Teacher spread0.228 · 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.

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

Citations5
Published2024
Admission routes1
Has abstractyes

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