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Record W4405362573 · doi:10.1126/sciadv.ads0587

Cretaceous coastal mountain building and potential impacts on climate change in East Asia

2024· article· en· W4405362573 on OpenAlexaff
Jianhua Li, Shuwen Dong, Guochun Zhao, Peter A. Cawood, Stephen T. Johnston, Jian Zhang, Yujia Xin

Bibliographic record

VenueScience Advances · 2024
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicGeological and Geochemical Analysis
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsGeologyAridCrustTectonicsClimate changeCretaceousMountain formationPrecipitationEarth scienceClimate modelMountain range (options)East AsiaClimatologyPaleontologyPhysical geographyOceanographyGeographyChinaMeteorology

Abstract

fetched live from OpenAlex

Crustal thickness and elevation variations control mountain building and climate change at convergent margins. As an archetypal Andean-type convergent margin, eastern Asia preserves voluminous magmas ideal for quantifying these processes and their impacts on climate. Here, we use Sr/Y and Ce/Y proxies to show that the crust experienced alternating thickening and thinning during the Late Mesozoic. We identify a noticeably thickened (50 to 55 kilometers) crust associated with tectonic shortening at 120 to 105 million years, corresponding to a >2500-meter-high coastal mountain range. Using climate simulation with the Community Earth System Model, we demonstrate that the mountain uplift changed Asian atmospheric circulation and precipitation patterns, increased inland aridity (~15%), and prompted the eastward desert expansion, contributing substantially to the arid zonal belt across mid- to low-latitude Asia. These findings-compatible with independent geological, geophysical, and climatic observations-have global implications for broadening our understanding of Earth-system interactions in the Cretaceous greenhouse world.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.502
Threshold uncertainty score0.572

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.012
GPT teacher head0.250
Teacher spread0.238 · 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.

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

Citations37
Published2024
Admission routes1
Has abstractyes

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