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Record W2011569431 · doi:10.1029/2007gl032997

Salmon‐driven bed load transport and bed morphology in mountain streams

2008· article· en· W2011569431 on OpenAlexaff
Marwan A. Hassan, Allen S. Gottesfeld, David R. Montgomery, Jon Tunnicliffe, Garry K. C. Clarke, Graeme Wynn, Hale Jones‐Cox, Ronald Poirier, Erland A. MacIsaac, Herb Herunter, Steve J. Macdonald

Bibliographic record

VenueGeophysical Research Letters · 2008
Typearticle
Languageen
FieldEnvironmental Science
TopicHydrology and Sediment Transport Processes
Canadian institutionsFisheries and Oceans CanadaSkeena Fisheries CommissionUniversity of British Columbia
Fundersnot available
KeywordsBed loadSTREAMSSediment transportHydrology (agriculture)SedimentRiver morphologyEnvironmental scienceChannel (broadcasting)AlluviumBedformStream bedEcosystemGeologyDrainage basinEcologyGeomorphologyGeographyBiologyGeotechnical engineering

Abstract

fetched live from OpenAlex

Analyses of bed load transport data from four streams in British Columbia show that the activity of mass spawning salmon moved an average of almost half of the annual bed load yield. Spawning‐generated changes in bed surface topography persisted from August through May due to lack of floods during the winter season, defining the bed surface morphology for most of the year. Hence, salmon‐driven bed load transport can substantially influence total sediment transport rates, and alter typical alluvial reach morphology. The finding that mass‐spawning fish can dominate sediment transport in mountain drainage basins has fundamental implications for understanding controls on channel morphology and aquatic ecosystem dynamics, as well as stream responses to environmental change and designing river restoration programs for channels that have, or historically had large spawning runs.

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)0.001
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 score0.269
Threshold uncertainty score0.534

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
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 head0.023
GPT teacher head0.277
Teacher spread0.254 · 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

Citations126
Published2008
Admission routes1
Has abstractyes

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