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Record W2725068739 · doi:10.1002/esp.4274

Experiments on the morphological controls of velocity inversions in bedrock canyons

2017· article· en· W2725068739 on OpenAlexafffund
Brendan Hunt, Jeremy G. Venditti, Eva Kwoll

Bibliographic record

VenueEarth Surface Processes and Landforms · 2017
Typearticle
Languageen
FieldEnvironmental Science
TopicHydrology and Sediment Transport Processes
Canadian institutionsSimon Fraser University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsCanyonBedrockGeologySillFlow (mathematics)GeomorphologyChannel (broadcasting)Hydrology (agriculture)MechanicsPetrologyGeotechnical engineeringPhysics

Abstract

fetched live from OpenAlex

Abstract A better understanding of bedrock incision mechanisms and processes is essential to the study of long‐term landscape evolution. Yet, little is known about flow dynamics in bedrock rivers, limiting our ability to make realistic predictions of local bedrock incision rates. A recent investigation of flow through bedrock canyons of the Fraser River revealed that plunging flows, defined by the downward‐directed movement of near surface flow toward the channel bed, occur in channels that have low width‐to‐depth ratios. Plunging flows occur into deep scour pools, which are often coincident with lateral constrictions and channel spanning submerged ridges (sills). A phenomenological investigation was undertaken to reproduce the flow fields observed in the Fraser canyons and to explore morphological controls on the occurrence and relative strength of plunging flow in bedrock canyons. Our observations show that the plunging flow structure can be produced along a scour pool entrance slope by accelerating the flow at the canyon entrance either over submerged sills or through lateral constrictions. Plunging flow appears to be a function of convective deceleration into a scour pool which can be enhanced by sill height, the amount of the channel width that is constricted, pool entrance slope, discharge, and a reduction in channel width‐to‐depth ratio. Plunging flow greatly enhances the potential for incision to occur along the channel bed and is an extreme departure from the assumptions of steady, uniform flow in bedrock incision models, highlighting the need for improved formulations that account for fluid flow. Copyright © 2017 John Wiley & Sons, Ltd.

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 score0.042
Threshold uncertainty score0.908

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 head0.022
GPT teacher head0.247
Teacher spread0.225 · 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
Published2017
Admission routes2
Has abstractyes

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