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Large Wood in Rivers

2017· reference-entry· en· W2790419720 on OpenAlexaboutno aff
Ellen Wohl

Bibliographic record

VenueEnvironmental science · 2017
Typereference-entry
Languageen
FieldEnvironmental Science
TopicHydrology and Sediment Transport Processes
Canadian institutionsnot available
Fundersnot available
KeywordsCoarse woody debrisFloodplainLarge woody debrisEnvironmental scienceDebrisChannel (broadcasting)HabitatEcologyAbundance (ecology)Substrate (aquarium)STREAMSEndangered speciesHydrology (agriculture)GeographyRiparian zoneGeology

Abstract

fetched live from OpenAlex

Large wood consists of downed, dead pieces of wood. Although different size definitions have been proposed, the most widely used is pieces ³ 10 cm diameter and 1 m length. Many works refer to large woody debris, but because debris typically has negative connotations, investigators increasingly use instream wood or large wood. Systematic scientific studies of large wood in river channels and floodplains began during the late 1970s, mostly in the northwestern United States and southwestern Canada. Knowledge of wood characteristics in these regions still exceeds understanding of wood in other environments. The historical development of large wood studies partly reflects the continued existence of extensive forest cover in these portions of North America and partly reflects the concern with endangered populations of salmonid fishes in many western coastal rivers of North America. Like many other fish species, salmonids benefit from the habitat associated with large wood, and this connection was a focus of the earliest wood studies. Increases in aquatic habitat abundance and diversity are one of many environmental benefits associated with large wood in channels and floodplains. Wood in channels creates flow resistance and deflects current toward the channel bed and banks, enhancing pool volume and the diversity of hydraulics and bed substrate and increasing the retention of organic matter and dissolved and particulate nutrients. Wood can enhance the exchange of water between the channel and underlying hyporheic zone, with attendant benefits for water temperature and chemistry and the abundance of macroinvertebrates. Wood creates spatial heterogeneity of channel form and enhances the connectivity between channels and floodplains by deflecting flow and suspended sediment beyond the channel and onto the floodplain. Historical descriptions of rivers throughout forested regions emphasize the enormous volumes of wood within channels and floodplains and the associated abundance of features such as floodplain wetlands. Most of the wood historically present throughout forested rivers of the temperate zone has been removed for flood control, navigation, and enhanced conveyance of cut logs being floated downstream to sawmills. This ubiquitous and sustained removal has led to widespread river metamorphosis and loss of river diversity and resilience to disturbances. Consequently, reintroduction and/or retention of wood, along with minimizing hazards from mobile wood, is now emphasized in river management. The number and geographic diversity of wood-related studies has exploded in the early 21st century. This tremendous increase has been facilitated by advances in ground- and space-based remote sensing imagery, active and passive techniques for tracing the movement of individual pieces of large wood, and increasingly complex numerical models that simulate diverse processes by which wood enters rivers and moves within river networks. Important gaps remain between public perceptions of wood in rivers, which tend to be largely negative, and scientific appreciation of wood in rivers. The classification of works cited here into specific headings and subheadings is partly arbitrary and subjective because many of the works address multiple aspects of large wood and could be placed under multiple subheadings.

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 categoriesMeta-epidemiology (narrow), Science and technology studies, Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.713
Threshold uncertainty score1.000

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 studies0.0010.003
Scholarly communication0.0000.001
Open science0.0020.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0150.004

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 head0.010
GPT teacher head0.227
Teacher spread0.217 · 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

Citations1
Published2017
Admission routes1
Has abstractyes

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