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Record W2301566065

Sediment Transport in a Northern Regulated Semi-alluvial River

2011· article· en· W2301566065 on OpenAlexaboutno aff
CD Rennie, Ahsan, M. E. St. Laurent

Bibliographic record

VenueProceedings of the 34th World Congress of the International Association for Hydro- Environment Research and Engineering: 33rd Hydrology and Water Resources Symposium and 10th Conference on Hydraulics in Water Engineering · 2011
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicSoil erosion and sediment transport
Canadian institutionsnot available
Fundersnot available
KeywordsBed loadHydrology (agriculture)SedimentBedrockSediment transportSuspended loadAlluviumPermafrostGeologyAggradationDeposition (geology)Current (fluid)Environmental scienceGeomorphologyOceanographyFluvialGeotechnical engineering
DOInot available

Abstract

fetched live from OpenAlex

The Lower Nelson River in northern Manitoba, Canada is regulated by Manitoba Hydro with three hydroelectric dams as well as diversion from the Churchill River. The river is partially controlled by bedrock and is incising into glacio-marine tills. The surrounding landscape is boreal forest with discontinuous permafrost. In preparation for the proposed Keeyask Generating Station at Gull Rapids, sediment transport throughout both a riverine reach and the lacustrine/reservoir reach upstream of the current dams was characterized by means of three years of physical sampling of suspended load and bedload. Both suspended load and bedload were low due to the influence of upstream lakes. Suspended sediment was generally wash load, and rating curves displayed distinct hysteresis, suggesting seasonal depletion of sediment supply. Up to 2/3 of the sediment transported in the upstream riverine reach was deposited in the existing reservoir downstream of Gull Rapids.

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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.606
Threshold uncertainty score0.793

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.001
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.020
GPT teacher head0.199
Teacher spread0.180 · 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

Citations1
Published2011
Admission routes1
Has abstractyes

Explore more

Same venueProceedings of the 34th World Congress of the International Association for Hydro- Environment Research and Engineering: 33rd Hydrology and Water Resources Symposium and 10th Conference on Hydraulics in Water EngineeringSame topicSoil erosion and sediment transportFrench-language works237,207