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Record W4240517327 · doi:10.1002/9781118451410.ch3

Water and Sediment in Large Rivers

2020· other· en· W4240517327 on OpenAlexaff
Avijit Gupta, Olav Slaymaker, Wolfgang J. Junk

Bibliographic record

Venuenot available
Typeother
Languageen
FieldEarth and Planetary Sciences
TopicGeological formations and processes
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsHydrology (agriculture)SedimentEnvironmental scienceDrainage basinPrecipitationFluvialGeologyDischargeSediment transportLandformGeomorphologyStructural basinGeotechnical engineeringMeteorology

Abstract

fetched live from OpenAlex

A large river basin includes actively eroding landforms which provide a significant volume of sediment. This chapter introduces the general sources of water and sediment for rivers. River discharge is computed as the volume of water passing a given point on the river in unit time. Of all the properties of a large river, a high discharge is the one most expected. The primary source of pre-precipitation moisture is the atmosphere. Precipitation requires cooling of moist air by upward convection or mixing between two air masses of different temperature. Depending on the texture, fluvial sediment can be transported as dissolved load in solution, in suspension through the water column, and as bed load moving in traction along the river bottom. The total volume of sediment per unit time is considered as sediment load or sediment discharge. Sediment discharge varies with time, changes in vegetation cover, and anthropogenic alterations of the 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 score0.008
Threshold uncertainty score0.023

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0010.001
Scholarly communication0.0020.001
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0070.001

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 head0.010
GPT teacher head0.187
Teacher spread0.177 · 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

Citations0
Published2020
Admission routes1
Has abstractyes

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