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Record W3191737884 · doi:10.1029/2021gl093962

“Tiny Wiggles” in the Late Miocene Red Clay Deposits in the North‐East of the Tibetan Plateau

2021· article· en· W3191737884 on OpenAlexafffund
Rui Zhang, Xiaohao Wei, Vadim A. Kravchinsky, Leping Yue, Yan Zheng, Jie Qin, Lijun Yang, Minghao Ma, Feng Xian, Hujun Gong, Yunxiang Zhang, Xiaofan Liu

Bibliographic record

VenueGeophysical Research Letters · 2021
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicGeology and Paleoclimatology Research
Canadian institutionsUniversity of Alberta
FundersChinese Academy of SciencesNatural Sciences and Engineering Research Council of CanadaNational Natural Science Foundation of China
KeywordsMagnetostratigraphyGeologyEarth's magnetic fieldPlateau (mathematics)PrecessionPaleontologyGeomagnetic reversalAeolian processesSedimentary rockPaleomagnetismGeophysicsPhysicsMagnetic fieldAstronomy

Abstract

fetched live from OpenAlex

Abstract Small amplitude or short period geomagnetic anomalies known as “tiny wiggles” (TWs) are often hard to identify because of magnetic signal smoothing in the marine record of geomagnetic reversals. We report here the late Miocene record of geomagnetic reversals in the aeolian red clay sediments of Linxia Basin in China that enables us to identify two TWs. We performed magnetostratigraphy dating and used spectral analysis to distinguish orbital cycles in the records of magnetic susceptibility (MS) and sedimentary grain size (GS) and develop an orbitally tuned age model. The presence of two TWs in the study section, that correspond to C5n.2n‐3 and C5r.2r‐1, is confirmed by orbital calibration of our age model through recognition of eccentricity, obliquity and precession in MS and GS records.

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 score0.021
Threshold uncertainty score0.042

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)0.0010.000

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 head0.039
GPT teacher head0.282
Teacher spread0.243 · 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

Citations11
Published2021
Admission routes2
Has abstractyes

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