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Record W4252238824 · doi:10.24124/2005/bpgub298

The effects of floods and sockeye salmon on streambed morphology.

2005· dissertation· en· W4252238824 on OpenAlexfundaboutno aff
Ronald Poirier

Bibliographic record

Venuenot available
Typedissertation
Languageen
FieldEnvironmental Science
TopicHydrology and Sediment Transport Processes
Canadian institutionsnot available
FundersUniversity of Northern British Columbia
KeywordsThalwegRiffleSTREAMSFlood mythGeologyRiver morphologyHydrology (agriculture)Structural basinGeomorphologyGeographyGeotechnical engineeringSedimentArchaeology

Abstract

fetched live from OpenAlex

Streambed changes resulting from floods and spawning activity of sockeye salmon were monitored in two gravel bed streams in Stuart-Takla Experimental Watersheds of the Upper Fraser River basin, British Columbia, Canada. The streams have a forced pool-riffle morphology, and are utilized yearly by 7,000 to 10,000 sockeye salmon for spawning. Streambed mapping was performed before and after nival floods, summer floods and sockeye salmon spawning events in 1996 and 1997. Flood transport moves gravel out of pools, increases gravel bar heights, creates scour holes, and establishes a distinct thalweg. Sockeye spawning, which follows the floods, removes gravel from the edges and surface of the bars, and fills in the pools, scour holes and thalweg. The stream morphology is thus altered in opposing fashion by two different processes. It was found that the cut and fill volumes are similar in magnitude but that the two processes affect the stream in a very different manner.

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 score0.118
Threshold uncertainty score0.234

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
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 head0.003
GPT teacher head0.219
Teacher spread0.217 · 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
Published2005
Admission routes2
Has abstractyes

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