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Record W3159026136 · doi:10.1139/er-2020-0126

Small hydraulic structures, big environmental problems: is it possible to mitigate the negative impacts of culverts on stream biota?

2021· article· en· W3159026136 on OpenAlexvenueno aff
Piotr Frankiewicz, Artur Radecki‐Pawlik, Andrzej Wałęga, Małgorzata Łapińska, Adrianna Wojtal‐Frankiewicz

Bibliographic record

VenueEnvironmental Reviews · 2021
Typearticle
Languageen
FieldEnvironmental Science
TopicHydrology and Sediment Transport Processes
Canadian institutionsnot available
FundersUniwersytet RzeszowskiU.S. Fish and Wildlife ServiceUtah State University
KeywordsCulvertBiotaHabitatEnvironmental scienceEcosystemSTREAMSHydrology (agriculture)EcologyEngineeringGeotechnical engineeringComputer scienceBiology

Abstract

fetched live from OpenAlex

This study is a broad and critical review of the transdisciplinary literature on the construction of culverts and their impacts on stream hydrology and geomorphology as well as on stream habitats and biota. For engineers, a culvert is a structure, usually of the tunnel type, that transfers a stream or open drain under a road, railway line, or other obstacle from one side to the other. In fact, culverts are complex hydraulic structures whose impacts on stream ecosystems must be evaluated and understood before they are designed. The objective of this paper is to analyse and discuss recent knowledge about culvert functioning in terms of their negative effects on the passage of freshwater biota, particularly fish, and on entire stream ecosystems. We present the results of many studies showing that improperly designed culverts are barriers for migrating animals and usually have serious ecological consequences (mainly disturbances to fish life history). We also pay attention to different culvert modification methods that increase their passability for organisms and mitigate the impacts of culverts on the surrounding environment. The other purpose of this review is therefore to emphasize that the integration of the knowledge and professional experience of biologists and ecologists with those of river managers, river engineers, hydraulic engineers, hydrologists, and geomorphologists is necessary to design culverts that preserve the natural properties of streams.

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)0.002
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: Review · Consensus signal: Review
Teacher disagreement score0.002
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0010.002
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.001

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 head0.026
GPT teacher head0.239
Teacher spread0.213 · 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
GenreReview

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

Citations28
Published2021
Admission routes1
Has abstractyes

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