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Record W4312140094 · doi:10.1002/esp.5534

Delayed neck cutoff in the meandering Black River of the Qinghai–Tibet plateau

2022· article· en· W4312140094 on OpenAlexaff
Zhiwei Li, Peng Gao, Yuchi You, Alvise Finotello, Alessandro Ielpi

Bibliographic record

VenueEarth Surface Processes and Landforms · 2022
Typearticle
Languageen
FieldEnvironmental Science
TopicHydrology and Sediment Transport Processes
Canadian institutionsOkanagan University CollegeUniversity of British Columbia, Okanagan CampusUniversity of British Columbia
FundersFundamental Research Funds for the Central UniversitiesNational Natural Science Foundation of China
KeywordsMeander (mathematics)GeologySinuosityCutoffFluvialPlateau (mathematics)GeomorphologyHydrology (agriculture)GeometryGeotechnical engineeringPhysics

Abstract

fetched live from OpenAlex

Abstract Neck cutoffs in meandering rivers have long been thought to occur when the neck width ( b ) approximates the mean width of the parent channel ( W ). Empirical evidence for this paradigm is scarce, however, because tracking late‐stage evolution of meander bends prior to cutoff at sufficient temporal resolutions is difficult in natural rivers. In this study, we captured field and photogrammetric data depicting temporal changes of the banklines forming the neck of one meander bend, and values of the narrowest neck width for 14 highly sinuous bends, in the Black River of the Qinghai–Tibet Plateau (China) over nearly four decades. Results show that the duration of bend evolution from the classic morphological threshold ( b ≈ W ) to the occurrence of neck cutoff could range from 52 to 161 years. This long period has hitherto been ignored by fluvial geomorphologists, and needs to be included in forthcoming kinematic and hydrodynamic models of meander evolution. Further analyses indicate that the neck narrowing process is autogenous at the local bend scale, neither controlled by the maximum bend sinuosity nor by the annual peak discharge.

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)0.000
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.069
Threshold uncertainty score0.796

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
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 head0.007
GPT teacher head0.191
Teacher spread0.184 · 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

Citations20
Published2022
Admission routes1
Has abstractyes

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