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A review of the effects of catch‐and‐release angling on black bass, <i>Micropterus</i> spp.: implications for conservation and management of populations

2007· review· en· W1978045690 on OpenAlexaff
M. J. Siepker, Kenneth G. Ostrand, Steven J. Cooke, David P. Philipp, David H. Wahl

Bibliographic record

VenueFisheries Management and Ecology · 2007
Typereview
Languageen
FieldEnvironmental Science
TopicFish Ecology and Management Studies
Canadian institutionsCarleton University
FundersUniversity of Illinois at Urbana-Champaign
KeywordsCatch and releaseMicropterusFishingFisheryBass (fish)PopulationFisheries managementBiologyRecreational fishingDemography

Abstract

fetched live from OpenAlex

Abstract This paper summarises recent peer‐reviewed literature addressing the effects of catch‐and‐release angling on black bass, Micropterus spp., to facilitate management and conservation of these fish. Traditionally, the effects of catch and release have been evaluated by measuring mortality. Many recent studies have measured sublethal effects on physiology and behaviour. There is also greater emphasis on adding more realism to sublethal catch‐and‐release experiments through angler involvement in research activities and by conducting studies in the field rather than in laboratory environments. Owing to these advances, there have been a number of recent findings, which are summarised here, related to air exposure, gear (e.g. circle hooks) and the weigh‐in procedure that are particularly relevant to black bass anglers, tournament organisers and fishery managers. Additional research is particularly needed for: (1) population‐level effects of angling for nesting fish; (2) population‐level effects of tournament‐associated mortality; (3) effectiveness of livewell additives for enhancing survival; (4) consequences of fish displacement in competitive events; (5) effects of weigh‐in procedures and other organisational issues on fish condition and survival; and (6) reducing barotrauma.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.643
Threshold uncertainty score0.711

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.001
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 head0.034
GPT teacher head0.282
Teacher spread0.248 · 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 designNot applicable
Domainnot available
GenreReview

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

Citations96
Published2007
Admission routes1
Has abstractyes

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