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Record W4229072242 · doi:10.1029/2021gl097623

Southeastern Tibetan Plateau Growth Revealed by Inverse Analysis of Landscape Evolution Model

2022· article· en· W4229072242 on OpenAlexaff
Xiaoping Yuan, Ruohong Jiao, Guillaume Dupont‐Nivet, Xiaoming Shen

Bibliographic record

VenueGeophysical Research Letters · 2022
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicGeology and Paleoclimatology Research
Canadian institutionsUniversity of Victoria
FundersNational Natural Science Foundation of China
KeywordsPlateau (mathematics)CenozoicGeologyNeogeneFluvialPaleogenePaleontologyErosionStructural basinPhysical geographyGeomorphologyGeography

Abstract

fetched live from OpenAlex

Abstract The Cenozoic history of the Tibetan Plateau topography is critical for understanding the evolution of the Indian‐Eurasian collision, climate, and biodiversity. However, the long‐term growth and landscape evolution of the Tibetan Plateau remain ambiguous, it remains unclear if plateau uplift occurred soon after the India‐Asia collision in the Paleogene (∼50–25 Ma) or later in the Neogene (∼20–5 Ma). Here, we reproduce the uplift history of the southeastern Tibetan Plateau using a 2D landscape evolution model, which simultaneously solves fluvial erosion and sediment transport processes in the drainage basins of the Three Rivers region (Yangtze, Mekong, and Salween Rivers). Our model was optimized through a formal inverse analysis with 20,000 forward simulations, which aims to reconcile the transient states of the present‐day river profiles. The results, compared to existing paleoelevation and thermochronologic data, suggest initially low elevations (∼300–500 m) during the Paleogene, followed by a gradual southeastward propagation of topographic uplift of the plateau margin.

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)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.047
Threshold uncertainty score0.094

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0010.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.028
GPT teacher head0.272
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 designSimulation or modeling
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

Citations17
Published2022
Admission routes1
Has abstractyes

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