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Record W2277242164 · doi:10.1139/cjes-2015-0209

Early Telychian (Silurian) marine siliciclastic red beds in the Eastern Yangtze Platform, South China: distribution pattern and controlling factors

2016· article· en· W2277242164 on OpenAlexvenueno aff
Jianbo Liu, Xiaohu Liu, Xiaole Zhang, Rong Jiayu

Bibliographic record

VenueCanadian Journal of Earth Sciences · 2016
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicPaleontology and Stratigraphy of Fossils
Canadian institutionsnot available
FundersNational Key Research and Development Program of ChinaChina Geological SurveyNational Natural Science Foundation of ChinaUniversity of Cincinnati
KeywordsSiliciclasticRed bedsGeologyPaleontologyOceanographyMonsoonSedimentary rockSedimentary depositional environmentStructural basin

Abstract

fetched live from OpenAlex

The distribution pattern of early Telychian (turriculatus–crispus graptolite biozone) red beds in the Eastern Yangtze Platform of South China is reconstructed based on regional geologic data. The red beds are developed in three areas, which are separated by regions without red deposition. The distribution pattern indicates that the Cathaysian Oldland was the provenance of sediment rich in ferric oxides, which are essential for the formation of red beds. Silurian marine siliciclastic red beds, both in China and worldwide, tended to develop during times of relatively low sea level. Coeval hematitic oolites that formed far from the coast may record a change from reducing to oxidizing conditions in the ocean. Furthermore, it is likely that a fall in global sea level, a transition from reducing to oxidizing conditions in the ocean, and a cooling climate, all of which were closely related to the early Telychian Valgu Event, promoted the global development of marine red beds during this 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)0.000
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 score0.045
Threshold uncertainty score0.089

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 studies0.0000.001
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 head0.017
GPT teacher head0.198
Teacher spread0.180 · 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

Citations29
Published2016
Admission routes1
Has abstractyes

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Same venueCanadian Journal of Earth SciencesSame topicPaleontology and Stratigraphy of FossilsFrench-language works237,207