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Record W2892199940 · doi:10.1038/s41467-018-06238-6

Abrupt drainage basin reorganization following a Pleistocene river capture

2018· article· en· W2892199940 on OpenAlexaff
Niannian Fan, Zhongxin Chu, Luguang Jiang, Marwan A. Hassan, Michael P. Lamb, Xingnian Liu

Bibliographic record

VenueNature Communications · 2018
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicGeology and Paleoclimatology Research
Canadian institutionsUniversity of British Columbia
FundersState Key Laboratory of Loess and Quaternary GeologyMinistry of Economy, Trade and IndustryNational Natural Science Foundation of ChinaNational Aeronautics and Space Administration
KeywordsBedrockGeologyDrainage basinRiver terracesPleistoceneTectonicsGeomorphologyHydrology (agriculture)DrainageStructural basinNatural (archaeology)FluvialPaleontologyEcologyGeography

Abstract

fetched live from OpenAlex

Abstract River capture is a dramatic natural process of internal competition through which mountainous landscapes evolve and respond to perturbations in tectonics and climate. River capture may occur when one river network grows at the expense of another, resulting in a victor that steals the neighboring headwaters. While river capture occurs regularly in numerical models, field observations are rare. Here we document a late Pleistocene river capture in the Yimeng Mountains, China that abruptly shifted 25 km 2 of drainage area from one catchment to another. River terraces and imbricated cobbles indicate that the main channel incised 27 m into granitic bedrock within 80 kyr, following the capture event, and upstream propagating knickpoints and waterfalls reversed the flow direction of a major river. Topographic analysis shows that the capture shifted the river basins far from topographic equilibrium, and active divide migration is propagating the effects of the capture throughout the landscape.

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 score0.017
Threshold uncertainty score0.035

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 studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.001
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 head0.017
GPT teacher head0.281
Teacher spread0.263 · 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

Citations83
Published2018
Admission routes1
Has abstractyes

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