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Record W4379471799 · doi:10.1038/s43247-023-00861-y

Fault systems impede incision of the Yarlung river into the Tibetan plateau

2023· article· en· W4379471799 on OpenAlexaff
Dongxu Cai, Xianyan Wang, Guangwei Li, Ruohong Jiao, Barry P. Kohn, Wenbin Zhu, Johan De Grave, Huayu Lu

Bibliographic record

VenueCommunications Earth & Environment · 2023
Typearticle
Languageen
FieldEarth and Planetary Sciences
Topicearthquake and tectonic studies
Canadian institutionsUniversity of Victoria
FundersNational Natural Science Foundation of ChinaMinistry of Science and Technology of the People's Republic of ChinaUniversity of MelbourneAustralian Government
KeywordsGeologyRiftTectonicsPlateau (mathematics)ThermochronologyFluvialGeomorphologyFault (geology)ErosionTerrainPaleontologyEarth scienceStructural basinGeography

Abstract

fetched live from OpenAlex

Abstract It is widely accepted that tectonics generally enhances river incision. However, why rivers have not incised further into orogenic plateaus to destroy terrains over long-time scales remains ambiguous. Here we hypothesize that the diverse nature of regional tectonics could have impeded river erosion, taking Yarlung River in Tibetan Plateau as a case. We constrain the incision history and effect of a tectonic rift on fluvial incision by low-temperature thermochronology. Results show focused cooling near the rift, but markedly reduced cooling in the upstream and downstream regions since ~7 Ma. This coincides with an episode of rapid exhumation of Eastern Himalaya Syntaxis downstream. We propose that these two co-phased tectonic systems resulting from accelerated extension of southern Tibetan Plateau prevented upstream migration of river knickpoints. Our study highlights that the activity of fault systems may hinder regional erosion, thereby facilitating the preservation of topography and high plateaus in active orogenic belts.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.011
Threshold uncertainty score0.021

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 science0.0000.000
Research integrity0.0000.000
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 head0.026
GPT teacher head0.229
Teacher spread0.202 · 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

Citations19
Published2023
Admission routes1
Has abstractyes

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