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Record W2076675196 · doi:10.1080/00028487.2012.664604

Effects of Decelerating and Accelerating Flows on Juvenile Salmonid Behavior

2012· article· en· W2076675196 on OpenAlexaff
Eva C. Enders, Michael H. Gessel, James J. Anderson, John G. Williams

Bibliographic record

VenueTransactions of the American Fisheries Society · 2012
Typearticle
Languageen
FieldEnvironmental Science
TopicFish Ecology and Management Studies
Canadian institutionsFisheries and Oceans Canada
FundersNorthwest Fisheries Science CenterNational Marine Fisheries ServiceNational Oceanic and Atmospheric Administration
KeywordsFlumeChinook windOncorhynchusFish <Actinopterygii>Environmental scienceJuvenileFlow (mathematics)FisheryBiologyMechanicsPhysicsEcology

Abstract

fetched live from OpenAlex

Abstract Migratory and resident fish species have evolved inherent flight responses to avoid potentially harmful situations. At many dams, fish screens or other structures have been installed to guide fish away from turbines or attract them to routes that will result in higher survival. Avoidance responses of fish to rapidly decelerating and accelerating flows at these structures have been repeatedly observed and can result in ineffective fish guidance. By using controlled flume experiments, we analyzed the avoidance behavior of actively migrating spring Chinook salmon Oncorhynchus tshawytscha smolts in relation to flow decelerations and accelerations. As smolts drifted into areas with decreasing velocities, they actively swam into the current; the larger was the change in water velocity with distance (spatial velocity gradient [SVG]), the faster was the swimming speed exhibited by smolts. Under accelerating flows, the response velocity varied significantly with flow conditions, but the median SVG at which smolts displayed an avoidance response was similar over all flows tested. For both decelerating and accelerating flows, the avoidance response occurred at an SVG of approximately 1 cm·s−1·cm−1. We suggest that this threshold is in part fixed by the energetically optimum swimming speed of the fish (∼1 body length/s).

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.050
Threshold uncertainty score0.289

Codex and Gemma teacher scores by category

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

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 head0.012
GPT teacher head0.222
Teacher spread0.210 · 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 teacher head, 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

Citations69
Published2012
Admission routes1
Has abstractyes

Explore more

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