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Record W2537472046 · doi:10.1017/cbo9781107110632.012

Mechanisms of Flow and Sediment Transport in Fluvial Ecosystems: Physical and Ecological Consequences

2016· book-chapter· en· W2537472046 on OpenAlexaff
Brett Eaton, Jordan S. Rosenfeld

Bibliographic record

VenueCambridge University Press eBooks · 2016
Typebook-chapter
Languageen
FieldEnvironmental Science
TopicHydrology and Sediment Transport Processes
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsFluvialSTREAMSSedimentChannel (broadcasting)Hydrology (agriculture)GeologyFlow (mathematics)EcosystemSediment transportCobbleEnvironmental scienceWater flowGeomorphologyEcologySoil scienceGeotechnical engineeringGeometryEngineering

Abstract

fetched live from OpenAlex

Introduction Anyone who has spent time in or around rivers will recognize that water flows in complex, ever-changing patterns that are in part determined by the physical shape and roughness of the stream boundaries. Low gradient, deep rivers may look almost like lakes, having very smooth water surfaces giving little indication that the water is flowing at all, while a great deal less water flowing through a steep, cobble-bedded mountain channel may form a turbulent, noisy maelstrom of whitewater (Figure 10.1). It is also true that these complex flow patterns imprint themselves physically upon the riverine environment by eroding, transporting and depositing sediment and organic material, thereby shaping the streams in which the water flows. Channels that have developed within large deposits of sediment (i.e., floodplains, fans and deltas) have alluvial channel boundaries , meaning that they consist of the sediment transported and deposited by the river itself. These systems are particularly dynamic, in that the boundaries of the stream channel evolve at rates that are appreciable on human timescales; the evolution of these boundaries is determined by the interplay between the forces and energy associated with the flux of water in the stream channel and the quantity and texture of sediment delivered to a stream channel from the surrounding drainage basin. In this way, the behavior of a stream at any given point can be influenced by processes happening anywhere in the drainage basin upstream. As the boundaries of these alluvial streams change, the aquatic ecosystems that they support must adapt. Hutchinson (1965) described the environment as a stage where plant and animal species play out the theater of life. In many ecosystems, such as boreal and tropical forests on land, or kelp forests in the ocean, plants form much of the three-dimensional structure that forms the ecological stage where individual growth, survival, predation, competition, and community dynamics occur. In streams and rivers, it is the physical structure of the channel itself that forms the dominant habitat template that constrains ecological processes and the adaptations of aquatic organisms. All aspects of the ecology of aquatic organisms – from behavior, growth, and reproduction to avoiding predation – are mediated by the attributes of the flowing water environment.

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)0.000
metaresearch head score (Gemma)0.000
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: Empirical · Consensus signal: none
Teacher disagreement score0.002
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.004
Scholarly communication0.0020.002
Open science0.0010.001
Research integrity0.0010.001
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 head0.011
GPT teacher head0.180
Teacher spread0.168 · 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
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

Citations4
Published2016
Admission routes1
Has abstractyes

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