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Record W2083786437 · doi:10.1111/jawr.12279

Geomorphic and Ecological Consequences of Riprap Placement in River Systems

2015· article· en· W2083786437 on OpenAlexaff
David A. Reid, Michael Church

Bibliographic record

VenueJAWRA Journal of the American Water Resources Association · 2015
Typearticle
Languageen
FieldEnvironmental Science
TopicHydrology and Sediment Transport Processes
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsRiprapRiparian zoneEnvironmental scienceErosionHydrology (agriculture)HabitatRiver morphologyChannel (broadcasting)SedimentSTREAMSBenthic zoneSediment transportEcosystemEcologyGeologyGeotechnical engineeringGeomorphologyComputer scienceBiology

Abstract

fetched live from OpenAlex

Abstract Riprap, consisting of large boulders or concrete blocks, is extensively used to stabilize streambanks and to inhibit lateral erosion of rivers, yet its effect on river morphology and its ecological consequences have been relatively little studied. In this paper, we review the available information, most of it culled from the “grey” literature. We use a simple one‐dimensional morphodynamic model as a conceptual tool to illustrate potential morphological effects of riprap placement in a gravel‐bed river, which include inhibition of local sediment supply to the channel and consequent channel bed scour and substrate coarsening, and downstream erosion. Riprap placement also tends to sever organic material input from the riparian zone, with loss of shade, wood input, and input of finer organic material. Available information on the consequences for the aquatic ecosystem mainly concerns effects on commercially and recreationally important fishes. The preponderance of studies report unfavorable effects on local numbers, but habitat niches created by openings in riprap can favorably affect invertebrates and some small fishes. There is a need for much more research on both morphological and ecosystem effects of riprap placement.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.014
Threshold uncertainty score0.143

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 studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.012
GPT teacher head0.215
Teacher spread0.203 · 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

Citations51
Published2015
Admission routes1
Has abstractyes

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