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Record W2003341533 · doi:10.1002/hyp.1186

Suspended sediment transport during tropical‐cyclone floods in Fiji

2002· article· en· W2003341533 on OpenAlexafffund
Ray Kostaschuk, James P. Terry, Rishi Raj

Bibliographic record

VenueHydrological Processes · 2002
Typearticle
Languageen
FieldEnvironmental Science
TopicFlood Risk Assessment and Management
Canadian institutionsUniversity of Guelph
FundersNatural Sciences and Engineering Research Council of CanadaUniversity of the Pacific
KeywordsSedimentHydrology (agriculture)Suspension (topology)Tropical cycloneHyperconcentrated flowSediment transportFlood mythErosionSuspended loadBed loadRiver floodEnvironmental scienceGeologyShear stressGeomorphologyGeotechnical engineeringOceanographyPhysicsGeographyMechanics

Abstract

fetched live from OpenAlex

Abstract Flow records, rising‐stage sediment samplers, and a sand suspension model are used to examine suspended sediment concentrations during major floods caused by tropical cyclones TC Joni and TC Kina in the Rewa River, Fiji. The highest concentrations of total suspended solids were measured during the early stages of TC Kina. The suspension model predicts higher sand concentrations for TC Kina compared with TC Joni because of the larger slope and higher shear stresses during Kina. Extremely high wash load concentrations early in TC Kina are at least partly due to remobilization of fine sediment deposited during the earlier TC Joni flood. Samples from the TC Kina had volumetric concentrations larger than 5%, indicating hyperconcentrated streamflows. Mass‐density shear stresses in the hyperconcentrated flows are up 1·6 times larger than clear‐water shear stresses, but they occur early during low stages of the flood and probably do not result in severe bed erosion. Copyright © 2002 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 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)0.000
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 score0.075
Threshold uncertainty score0.149

Distilled classifier scores by category (both heads)

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 head0.014
GPT teacher head0.213
Teacher spread0.199 · 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

Citations27
Published2002
Admission routes2
Has abstractyes

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