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Record W2070449671 · doi:10.1002/rra.878

Fine‐grained sediment in river systems: environmental significance and management issues

2005· article· en· W2070449671 on OpenAlexaffabout
Philip N. Owens, Ramón J. Batalla, Alison Collins, Basil Gómez, D. Murray Hicks, Arthur J. Horowitz, G. M. Kondolf, Mike Marden, M.J. Page, D. H. Peacock, Ellen L. Petticrew, Wim Salomons, Noel A. Trustrum

Bibliographic record

VenueRiver Research and Applications · 2005
Typearticle
Languageen
FieldEnvironmental Science
TopicHydrology and Sediment Transport Processes
Canadian institutionsUniversity of Northern British Columbia
FundersU.S. Army Corps of EngineersEuropean CommissionNational Oceanic and Atmospheric AdministrationU.S. Environmental Protection Agency
KeywordsSedimentEnvironmental scienceHabitatRiver managementHydrology (agriculture)Sustainable managementNatural (archaeology)EcologyEnvironmental resource managementGeologySustainabilityGeomorphology

Abstract

fetched live from OpenAlex

Abstract Fine‐grained sediment is a natural and essential component of river systems and plays a major role in the hydrological, geomorphological and ecological functioning of rivers. In many areas of the world, the level of anthropogenic activity is such that fine‐grained sediment fluxes have been, or are being, modified at a magnitude and rate that cause profound, and sometimes irreversible, changes in the way that river systems function. This paper examines how anthropogenic activity has caused significant changes in the quantity and quality of fine‐grained sediment within river systems, using examples of: land use change in New Zealand; the effects of reservoir construction and management in different countries; the interaction between sediment dynamics and fish habitats in British Columbia, Canada; and the management of contaminated sediment in USA rivers. The paper also evaluates present programmes and initiatives for the management of fine sediment in river systems and suggests changes that are needed if management strategies are to be effective and sustainable. Copyright © 2005 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)0.001
metaresearch head score (Gemma)0.004
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.025
Threshold uncertainty score0.049

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.005
Science and technology studies0.0010.004
Scholarly communication0.0050.004
Open science0.0010.001
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.020
GPT teacher head0.290
Teacher spread0.270 · 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

Citations683
Published2005
Admission routes2
Has abstractyes

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