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Record W2913811433

The Effects of Run-of-River Hydroelectric Power Schemes on Fish and Invertebrate Community Composition in Temperate Streams and Rivers

2017· article· en· W2913811433 on OpenAlexaff
Gary Bilotta, Niall G. Burnside, Matthew Turley, Jeremy C. Gray, Harriet G. Orr

Bibliographic record

VenueUniversity of Brighton Repository (University of Brighton) · 2017
Typearticle
Languageen
FieldEnvironmental Science
TopicFish Ecology and Management Studies
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsHydroelectricitySTREAMSInvertebrateTemperate climateFish <Actinopterygii>Environmental scienceFisheryComposition (language)Hydrology (agriculture)EcologyGeologyBiology
DOInot available

Abstract

fetched live from OpenAlex

Run-of-river (ROR) hydroelectric power schemes are often presumed to be less environmentally-damaging than large-scale storage schemes. However, there are currently only a limited number of peer-reviewed studies on their physical and ecological impact. This presentation will summarise the findings from a policy secondment, funded by the UK’s Natural Environment Research Council and the Environment Agency of England, which investigated the impacts of ROR hydroelectric power schemes on fish and invertebrate communities in temperate streams and rivers, using Before-After, Control-Impact (BACI) study designs. The study made use of routine environmental surveillance data collected as part of long-term national and international monitoring programmes at systematically-selected ROR hydroelectric power schemes and systematically-selected paired control sites. Five metrics of invertebrate community composition and six area-normalised metrics of fish community composition were analysed using linear mixed effects models. The results are discussed with respect to impacts from other sources of power, and recommendations are made for best-practice study design for future freshwater community impact studies.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesScience and technology studies
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.027
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0020.003
Scholarly communication0.0000.000
Open science0.0000.001
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 head0.003
GPT teacher head0.159
Teacher spread0.155 · 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

Citations0
Published2017
Admission routes1
Has abstractyes

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