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Record W2944404053 · doi:10.1002/nafm.10285

Rainbow Trout Migration and Use of a Nature-Like Fishway at a Great Lakes Tributary

2019· article· en· W2944404053 on OpenAlexafffundabout
Christopher M. Bunt, Bailey Jacobson

Bibliographic record

VenueNorth American Journal of Fisheries Management · 2019
Typearticle
Languageen
FieldEnvironmental Science
TopicFish Ecology and Management Studies
Canadian institutionsFisheries and Oceans Canada
FundersBruce Power
KeywordsRainbow troutFisheryTributaryFish migrationFish <Actinopterygii>BayTroutEnvironmental scienceGeographyBiologyCartography

Abstract

fetched live from OpenAlex

Abstract Rainbow Trout Oncorhynchus mykiss were monitored over two consecutive vernal migration periods at a nature-like fishway on the Beaver River, Ontario, to assess attraction efficiency, passage efficiency, multiple fish passage metrics, and interannual return rates from Georgian Bay, Lake Huron. Fishway evaluations have shifted to fill knowledge gaps related to the passage of nonsalmonids; however, surprisingly little work has been conducted with Rainbow Trout, with no known study assessing attraction or passage at a nature-like fishway. Attraction efficiency was 53% and passage efficiency was 100% in 2017; only two of the radio-tagged fish returned to the fishway in 2018. Upstream passage through the fishway required an average time of 152 ± 122 min. Fish spent 19–43 d upstream before returning to Lake Huron, where downstream passage required as little as 15 min. Overall, there were no significant relationships between any of the fish passage metrics and fish size or condition. These results can be used as a foundation for anadromous O. mykiss subspecies passage research and suggest that fisheries managers may need to adjust annual fishway counts. Future research should focus on developing methods to directly integrate temporal passage metrics into estimates of fishway efficiency.

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 score0.204
Threshold uncertainty score0.407

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 head0.005
GPT teacher head0.186
Teacher spread0.181 · 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

Citations10
Published2019
Admission routes3
Has abstractyes

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