MétaCan
Menu
Back to cohort
Record W1594732581 · doi:10.1029/2000wr000140

Hydraulic conditions in experimental rill confluences and scour in erodible soils

2002· article· en· W1594732581 on OpenAlexaff
Rorke B. Bryan, Nikolaus J. Kuhn

Bibliographic record

VenueWater Resources Research · 2002
Typearticle
Languageen
FieldEnvironmental Science
TopicHydrology and Sediment Transport Processes
Canadian institutionsThe Scarborough HospitalUniversity of Toronto
Fundersnot available
KeywordsConfluenceRillGeologyGeotechnical engineeringGeometryGeomorphologyTurbulenceFlow (mathematics)Hydraulic jumpErosionHydrology (agriculture)MechanicsMathematicsPhysics

Abstract

fetched live from OpenAlex

Few studies have examined processes active within rill networks or the effect of confluence geometry on rill sediment flux, and it is not clear if observations from river confluences also apply at rill scale. Laboratory experiments were carried out in artificial rectangular rill channels with fixed and erodible beds to identify the effects of confluence geometry on hydraulic conditions and scour patterns. Symmetrical and asymmetrical confluences with angles from 19° to 90° were used. Hydraulic changes in the confluence zone in fixed bed experiments included decreased flow velocity, increased shear velocity, transition from supercritical to subcritical, and from transitional to turbulent flow. Bed scour in the confluence zone resulted, generally increasing with confluence angle, but a more important influence was junction symmetry or asymmetry. Symmetrical confluences produced a symmetrical scour pattern mirroring the original channels, but in asymmetrical confluences scour was more complex, evolving to a symmetrical pattern with confluence angles higher than the original system. Results are consistent with channel evolution to an optimally branched system controlled by minimum power criteria, but validation requires testing with more precise velocity measurement.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.430
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0090.001

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 head0.045
GPT teacher head0.312
Teacher spread0.266 · 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; both teacher heads agree on what is shown here.

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

Citations29
Published2002
Admission routes1
Has abstractyes

Explore more

Same venueWater Resources ResearchSame topicHydrology and Sediment Transport ProcessesFrench-language works237,207