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Record W2004933235 · doi:10.1139/f10-021

Do in-stream restoration structures enhance salmonid abundance? A meta-analysis

2010· article· en· W2004933235 on OpenAlexafffundvenue
Sarah L. Whiteway, Pascale M. Biron, André Zimmermann, Oscar Venter, James W. A. Grant

Bibliographic record

VenueCanadian Journal of Fisheries and Aquatic Sciences · 2010
Typearticle
Languageen
FieldEnvironmental Science
TopicFish Ecology and Management Studies
Canadian institutionsConcordia University
FundersNatural Sciences and Engineering Research Council of CanadaFaculty of Arts and SciencesConcordia University
KeywordsRiffleStream restorationEnvironmental scienceHabitatRainbow troutLarge woody debrisBiomass (ecology)Abundance (ecology)DebrisTroutCoarse woody debrisRestoration ecologyEcologyElectrofishingHydrology (agriculture)FisheryFish <Actinopterygii>BiologyGeographyRiparian zoneGeology

Abstract

fetched live from OpenAlex

Despite the widespread use of stream restoration structures to improve fish habitat, few quantitative studies have evaluated their effectiveness. This study uses a meta-analysis approach to test the effectiveness of five types of in-stream restoration structures (weirs, deflectors, cover structures, boulder placement, and large woody debris) on both salmonid abundance and physical habitat characteristics. Compilation of data from 211 stream restoration projects showed a significant increase in pool area, average depth, large woody debris, and percent cover, as well as a decrease in riffle area, following the installation of in-stream structures. There was also a significant increase in salmonid density (mean effect size of 0.51, or 167%) and biomass (mean effect size of 0.48, or 162%) following the installation of structures. Large differences were observed between species, with rainbow trout ( Oncorhynchus mykiss ) showing the largest increases in density and biomass. This compilation highlights the potential of in-stream structures to create better habitat for and increase the abundance of salmonids, but the scarcity of long-term monitoring of the effectiveness of in-stream structures is problematic.

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 categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.727
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.023
GPT teacher head0.246
Teacher spread0.223 · 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.

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

Citations177
Published2010
Admission routes3
Has abstractyes

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