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Record W4323822301 · doi:10.1029/2022gl101054

A 1000‐Year Record of Temperature From Isotopic Analysis of the Deep Critical Zone in Central China

2023· article· en· W4323822301 on OpenAlexafffund
Hongxiu Wang, Han Li, Wei Xiang, Yanwei Lu, Huanhuan Wang, Wei Hu, Bingcheng Si, Scott Jasechko, Jeffrey J. McDonnell

Bibliographic record

VenueGeophysical Research Letters · 2023
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicGeology and Paleoclimatology Research
Canadian institutionsGlobal Institute for Water SecurityUniversity of Saskatchewan
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsStalagmitePaleoclimatologyGeologyIce coreSoil waterLoessPeriod (music)δ13CStable isotope ratioPhysical geographyMineralogyHydrology (agriculture)ClimatologyHolocenePaleontologyClimate changeSoil scienceOceanography

Abstract

fetched live from OpenAlex

Abstract Temperature proxies for paleoclimate reconstruction have been made typically via ice cores, tree rings, stalagmites, and lake sediments. While extremely useful, these proxies can be limited spatially. Here we sampled a 98 m “soil core” from Loess Plateau of China and examined the relationship between pore water isotopic values and hydroclimate history. We extracted soil pore water for δ 18 O, δ 2 H, and 3 H and measured chloride concentration. The 3 H‐peak at 6 m and chloride mass balance were used to turn depth into calendar year. A 1000 year span was revealed. δ 18 O and δ 2 H values between 14–50 m were anomalously low—bracketing well the Little Ice Age period from 1420 to 1870. The identification was consistent with other standard proxies in the region and showed the same temporal dynamics of temperature anomalies. Our study shows the potential of stable isotopes of soil water for paleoclimate reconstruction in deep soils.

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)0.000
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 score0.059
Threshold uncertainty score0.117

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
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)0.0000.000

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 head0.021
GPT teacher head0.295
Teacher spread0.274 · 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

Citations4
Published2023
Admission routes2
Has abstractyes

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