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Record W2585386061 · doi:10.1139/cjfas-2016-0410

Swimming performance of sauger (<i>Sander canadensis</i>) in relation to fish passage

2017· article· en· W2585386061 on OpenAlexvenueno aff
David Dockery, Thomas E. McMahon, Kevin M. Kappenman, Matthew D Blank

Bibliographic record

VenueCanadian Journal of Fisheries and Aquatic Sciences · 2017
Typearticle
Languageen
FieldEnvironmental Science
TopicFish Ecology and Management Studies
Canadian institutionsnot available
FundersBonneville Power AdministrationWisconsin Department of Natural Resources
KeywordsFlumeBiologyFish <Actinopterygii>FisheryOpen waterEnvironmental scienceRange (aeronautics)Animal scienceEcologyMechanicsPhysicsEngineeringFlow (mathematics)

Abstract

fetched live from OpenAlex

A lack of information on the swimming abilities of sauger (Sander canadensis), a highly migratory species particularly sensitive to habitat fragmentation, may inhibit the design of effective passage structures for this species. Passage success, maximum ascent distances, and maximum sprint velocities of sauger were estimated in an open-channel flume over a range of water velocities (51, 78, and 92 cm·s−1) and temperatures (10.0, 14.3, and 18.3 °C) to assess swimming performance. Passage success was high (91%) over all test velocities, as was the maximum instantaneous burst velocity (219 cm·s−1). Water temperature and body size had little effect on swimming performance. Sauger transitioned from steady, sustained swimming to unsteady, burst–glide, or steady burst swimming at 97 cm·s−1. Sauger were capable of sustained sprints of 124 cm·s−1 over 15 s duration in a swim chamber. Results suggest passage structures with water velocities less than 97 cm·s−1 should provide high probability of successful passage of adult sauger, whereas structures with water velocities exceeding 219 cm·s−1 may be impassable.

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)0.000
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.984
Threshold uncertainty score0.032

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
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 head0.016
GPT teacher head0.211
Teacher spread0.195 · 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

Citations14
Published2017
Admission routes1
Has abstractyes

Explore more

Same venueCanadian Journal of Fisheries and Aquatic Sciences→Same topicFish Ecology and Management Studies→French-language works237,207→