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Record W4321500970 · doi:10.5194/egusphere-egu23-2123

A typology of hydraulic barriers to salmon migration in a bedrock river

2023· preprint· en· W4321500970 on OpenAlexaffabout
Morgan Wright, Max Hurson, David A. Patterson, Kendra A. Robinson, Jake Baerg, Jeremy G. Venditti

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldEnvironmental Science
TopicFish Ecology and Management Studies
Canadian institutionsDeep River Science AcademyFisheries and Oceans CanadaSimon Fraser University
Fundersnot available
KeywordsCanyonBedrockHydrology (agriculture)Channel (broadcasting)GeologyFlow (mathematics)FisheryGeomorphologyGeotechnical engineeringGeometry

Abstract

fetched live from OpenAlex

Each year a variable portion of adult Pacific salmon in the Fraser River, British Columbia, Canada die trying to retrace and ascend the river network to their natal spawning grounds. A major factor in migration failure is the severe hydraulic conditions experienced in the Fraser Canyon where encounter velocities can exceed upstream swim speeds of adult salmon, creating a migration barrier. Hydraulic barriers are defined as reaches of river where upstream fish migration is delayed due to high water velocity. A few barriers have been identified along the river and have structures in place designed to help facilitate fish passage. We explore other locations in the Fraser River that are apt to be hydraulic barriers to fish migration based on measured centerline velocity. We classify the barriers as either 1) plunging flows in canyons where the channel is deep and the fastest velocities are observed deep in the water column, 2) rapids where flow is fast and shallow over one or more bedrock steps, or 3) overfalls where fast flow occurs over a step with a substantial drop in elevation. We used drone footage at various discharges and Large-Scale Particle Image Velocimetry (LSPIV) to examine flow structure at typical plunging flows, rapids and overfalls. Surface velocities for the discharges when salmon species are known to be migrating upstream were then compared with published salmon swimming capabilities to determine which locations are likely to create the greatest barriers to salmon migration. We find that there are twenty-two sites, sixteen measured and six suspected high velocity locations, that are potential hydraulic barriers. Overfalls present the greatest barrier to salmon migration, creating vertical barriers in addition to high velocity across the entire width of the channel in narrow laterally constricted reaches. Rapids have high velocity in the segments of the water column where salmon typically swim, but often have back eddies along the banks for fish to rest. Plunging flows in canyons have high depth-averaged velocities, and higher bank velocities as a result of turbulent upwelling along the walls, but typically lower surface velocities than the overfalls and rapids. Pacific salmon populations are already threatened by external factors – such as climate change, habitat degradation, fishing, and disease – and cannot afford to have these impacts amplified by additional barriers to migration. Our observations provide important information for salmon conservation and can be used to better understand salmon migration which in turn helps to inform future mitigation efforts to improve salmon survival rates.

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)0.002
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 score0.014
Threshold uncertainty score0.028

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.015
GPT teacher head0.244
Teacher spread0.229 · 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
Published2023
Admission routes2
Has abstractyes

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