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Record W2970870957 · doi:10.1002/nafm.10357

The Importance of Live-Well Transport in the Physiological Disturbance Experienced by Smallmouth Bass in Tournaments on Large Water Bodies

2019· article· en· W2970870957 on OpenAlexaffabout
Thomas C. Brooke, Connor W. Elliott, Jeremy P. Holden, Yuxiang Wang, Rachael L. Hornsby, Bruce L. Tufts

Bibliographic record

VenueNorth American Journal of Fisheries Management · 2019
Typearticle
Languageen
FieldEnvironmental Science
TopicFish Ecology and Management Studies
Canadian institutionsMinistry of Natural Resources and ForestryQueen's UniversityHatch (Canada)Fleming College
Fundersnot available
KeywordsMicropterusBass (fish)FisheryRecreationFishingDisturbance (geology)BiologyEcologyEnvironmental science

Abstract

fetched live from OpenAlex

Abstract Competitive fishing has become an important element of recreational fisheries for black bass Micropterus spp. in North America. The vast majority of competitive events involve a “live-release” format, where fish are held in a boat's live well after being angled and are then released following the weigh-in. We examined the frequency and importance of physical impacts between Smallmouth Bass Micropterus dolomieu and the walls of the live well when tournaments are held on large water bodies. Using an experimental live well that included a video recorder, we determined the number of collisions between Smallmouth Bass and the walls of the live well when a 5.6-m boat was driven on Lake Ontario. During these experiments, 10 of 28 Smallmouth Bass lost equilibrium and became inverted. Live-well transport also resulted in elevations of intracellular enzymes in blood plasma that were used as indicators of cell damage. The results of these experiments indicate that physical impacts with the walls of the live well may be an important factor contributing to the physiological disturbance experienced by Smallmouth Bass in tournaments on large water bodies. Our results also show that this disturbance can be reduced by simply padding the live well. These findings have important implications for fisheries managers, as well as the competitive angling community.

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 score0.013
Threshold uncertainty score0.026

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 studies0.0010.000
Scholarly communication0.0010.000
Open science0.0000.000
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 head0.006
GPT teacher head0.199
Teacher spread0.192 · 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

Citations6
Published2019
Admission routes2
Has abstractyes

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