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Record W4402131555 · doi:10.1029/2024gl109109

Autogenic Formation of Bimodal Grain Size Distributions in Rivers and Its Contribution to Gravel‐Sand Transitions

2024· article· en· W4402131555 on OpenAlexaff
Chenge An, Gary Parker, Jeremy G. Venditti, Michael P. Lamb, Marwan A. Hassan, Hiroshi Miwa, Xudong Fu

Bibliographic record

VenueGeophysical Research Letters · 2024
Typearticle
Languageen
FieldEnvironmental Science
TopicHydrology and Sediment Transport Processes
Canadian institutionsUniversity of British ColumbiaSimon Fraser University
FundersUniversity of Illinois SystemChina Association for Science and TechnologyNational Natural Science Foundation of ChinaCalifornia Institute of Technology
KeywordsGrain sizeGeologyGeomorphology

Abstract

fetched live from OpenAlex

Abstract Riverbeds often fine downstream, with a gravel‐bedded reach, a relatively abrupt gravel‐sand transition (GST), and a sand‐bedded reach. Underlying this behavior, bed grain size distributions are often bimodal, with a relative paucity (gap) around the range 1–5 mm. There is no general morphodynamic model capable of producing the grain size gap and gravel‐sand transition autogenically from a unimodal sediment supply. Here we use a one‐dimensional morphodynamic model including size‐specific bedload and suspended load transport, to show that bimodality readily evolves autogenically even under unimodal sediment feed. A GST forms when we include a floodplain width that abruptly increases at some point. Upstream of the transition, non‐gap gravel ceases to move and gap sediment is preferentially transported. At the transition, non‐gap sand rapidly deposits from suspension, enhancing gap sediment mobility and diluting its presence on the bed.

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 score0.003
Threshold uncertainty score0.006

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 communication0.0010.000
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 head0.016
GPT teacher head0.287
Teacher spread0.271 · 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

Citations5
Published2024
Admission routes1
Has abstractyes

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