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Record W7049252851

Network dynamics and origin of anastomosis, upper Columbia River, British Columbia, Canada.

2010· dissertation· en· W7049252851 on OpenAlexaboutno aff

Bibliographic record

VenueUtrecht University Repository (Utrecht University) · 2010
Typedissertation
Languageen
FieldPhysics and Astronomy
TopicMagnetic confinement fusion research
Canadian institutionsnot available
Fundersnot available
KeywordsChannel (broadcasting)Current (fluid)NektonLong-term predictionRadar tracker
DOInot available

Abstract

fetched live from OpenAlex

The upper Columbia River is an anastomosing river. Anastomosing rivers consist of a network of channels enclosing floodbasins. The factors that determine the configuration of river networks such as the upper Columbia River are unknown. Additionally the origin of anastomosing rivers is unclear. Two main theories exist on the origin of anastomosis; Nanson and Knighton (1996) state that it is hydraulically more efficient to transport flow and sediment in multiple channels instead of a single channel, for rivers that are unable to adjust their slope. Makaske (2001) states that frequent avulsion and/or slow abandonment of old channels leads to continuing coexistence of multiple channels. The Nanson and Knighton (1996) hypothesis on the origin of anastomosis implicates that anastomosis is an equilibrium channel pattern, whereas Makaske (2001) states that it is a disequilibrium channel pattern.\nThe aim of this research was to explain the network dynamics and origin of anastomosis in the upper Columbia River.\nField measurements were performed in the upper Columbia River to collect model input and to test model concepts. A river network model was developed to address the factors that determine the configuration of river networks, and to test the current hypotheses on the origin of anastomosis. \nThe discharge distribution through a river network is mainly determined by the network topology. Additionally, the bifurcation (morpho)dynamics affect the discharge distribution through a river network. The bifurcation dynamics are determined by downstream slope differences between bifurcates, upstream bend effect and amount of downstream confluences and bifurcations.\nWhen a constant Chézy roughness is assumed to represent bed roughness, it is more efficient to transport flow and sediment in multiple channels than in a single channel. When a constant Nikuradse roughness length is assumed to represent bed roughness, it is more efficient to transport flow and sediment in a single channel. The effect on network configuration is largest for the latter roughness assumption. Additionally, in natural rivers the constant Nikuradse roughness length assumption is more plausible since the wall friction is relatively large in small channels compared to large channels. Hence the Nanson and Knighton (1996) hypothesis on the origin of anastomosis does not hold for the upper Columbia River.\nA network model is applied to a 21km reach of the upper Columbia River that is characterized by well developed anastomosis. The steepest part of this reach is characterized by the best developed anastomosis. Model results indicate that a sediment pulse of 150yr of on average 3 times the transport capacity is able to reproduce the bed slope of this steep reach. It follows that the Makaske (2001) hypothesis on the origin of anastomosis explains anastomosis in the upper Columbia River. Sediment pulses induce a high avulsion rate, and therefore cause the coexistence of multiple channels on the floodplain. In the absence of a new sediment pulse the river network will most likely evolve towards a single main channel again. Consequently anastomosis in the upper Columbia River is a disequilibrium channel pattern. The cause of anastomosis in other anastomosing river systems might possibly also be a constant overload of sediment or a temporary sediment pulse.

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 categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.595
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0140.000

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 head0.169
Teacher spread0.165 · 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 designNot applicable
Domainnot available
GenreOther

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
Published2010
Admission routes1
Has abstractyes

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