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Record W4283123382 · doi:10.1029/2022gl098487

Amplification of Plunging Flows in Bedrock Canyons

2022· article· en· W4283123382 on OpenAlexafffund
Max Hurson, Jeremy G. Venditti, Colin D. Rennie, Eva Kwoll, Kirsti Fairweather, Dan R. W. Haught, Kyle M. Kusack, Michael Church

Bibliographic record

VenueGeophysical Research Letters · 2022
Typearticle
Languageen
FieldEnvironmental Science
TopicHydrology and Sediment Transport Processes
Canadian institutionsUniversity of British ColumbiaCanadian Hydrographic ServiceUniversity of OttawaUniversity of VictoriaSimon Fraser University
FundersNatural Sciences and Engineering Research Council of CanadaNational Geographic Society
KeywordsBedrockGeologyErosionCanyonRiver morphologyFlood mythHydraulicsHydrology (agriculture)GeomorphologySediment transportDischargeFloodplainBed loadSedimentHydraulic jumpOverbankFlow (mathematics)FluvialMechanicsGeotechnical engineeringDrainage basin

Abstract

fetched live from OpenAlex

Abstract Bedrock river erosion is driven by channel hydraulics, which are not well understood for complex morphologies. Many bedrock rivers exhibit a constriction‐pool‐widening (CPW) morphology associated with submerged plunging flows. These flows cause velocity profile inversions resulting in high velocities near the bed and low velocities on the water surface. The first observations documenting plunging flows were from relatively low discharge, and it is unclear whether they persist during floods. Here we show that plunging flows persist and get stronger at flood discharge, increasing bedrock erosion potential by particle impacts. Flood‐discharge plunging flows contact the bed and maintain high velocities farther through the CPW structure, and evacuate large volumes of sediment from the pools. These reach‐scale processes are not represented in large‐scale landscape evolution models, yet these erosion mechanisms set the pace of landscape evolution, begging for a re‐evaluation of process representation in landscape evolution models.

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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
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 head0.294
Teacher spread0.268 · 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 designBench or experimental
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

Citations7
Published2022
Admission routes2
Has abstractyes

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