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Record W7051923074

Potential Effects of the Indo-Pacific Lionfish Invasion on the Bahamian Lobster Fishery

2021· other· en· W7051923074 on OpenAlexaff

Bibliographic record

VenueAquaDocs (United Nations Educational, Scientific and Cultural Organization) · 2021
Typeother
Languageen
FieldPhysics and Astronomy
TopicMagnetic confinement fusion research
Canadian institutionsSimon Fraser University
Fundersnot available
KeywordsFishingEctothermAquatic animalInvertebrateNettingFish <Actinopterygii>
DOInot available

Abstract

fetched live from OpenAlex

Human-mediated introductions of species outside of their native range are becoming increasingly common and pose a significant threat to marine biodiversity (Ruiz et al. 1997, Molnar et al. 2008).When introduced species become invasive, they interfere with the ecological functioning of native communities (Grosholz 2002) and can cause ecological and economic impacts ranging from species extinction to altered industry and societal structure (Bax et al. 2003).Introduced by way of the southeastern United States, Indo-Pacific lionfishes (Pterois volitans and P. miles) have rapidly swept through the Caribbean region with the first recorded sighting in the Bahamas in 2004(Whitfield et al. 2002, Schofield 2009).They are now observed in densities far exceeding those of their native range (Green and Côté 2009) and can reduce recruitment of native fish species by up to 79% (Albins and Hixon 2008).The ecological impacts of the lionfish invasion have been the focus of most research to date, as these predatory fish consume a wide array of native fish and crustacean species (Morris and Akins 2009).Although many of these prey species are of economic importance, as of yet there has been virtually no investigation of the economic implications of the lionfish invasion.Lionfish could impact fisheries in at least three ways: i) By preying on larval and juvenile stages of species important to reef fisheries, lionfish could cause declines in recruitment to larger size classes, leading to a gradual reduction in landings.This is likely to be difficult to detect in the short term due to the relatively recent nature of the invasion.ii) Lionfish may compete with native reef predators for prey but may also compete spatially with a variety of native species for suitable shelter, and iii) Lastly, fishers anecdotally report slowing their pace when working around lionfish because of concerns over their venomous spines (E.B.H., unpublished data).Depending on the fishery and the type of gear used, lionfish in and around traps could lead to increased handling time, reducing fishing efficiency with concomitant drops in catch per unit effort and income.The main goal of this study is to examine the potential economic repercussions of this invasion by measuring its effect on the economically important spiny lobster (Panulirus argus) fishery of the Bahamas.

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 score0.208
Threshold uncertainty score0.414

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 science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0140.001

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 head0.008
GPT teacher head0.226
Teacher spread0.218 · 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

Citations0
Published2021
Admission routes1
Has abstractyes

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