MétaCan
Menu
Back to cohort
Record W2046429906 · doi:10.1139/f08-056

Quantifying invasion pathways: fish introductions from the aquarium trade

2008· article· en· W2046429906 on OpenAlexafffundvenueabout
E. L. Gertzen, Oriana Familiar, Brian Leung

Bibliographic record

VenueCanadian Journal of Fisheries and Aquatic Sciences · 2008
Typearticle
Languageen
FieldEnvironmental Science
TopicFish Ecology and Management Studies
Canadian institutionsMcGill University
FundersNatural Sciences and Engineering Research Council of CanadaNational Oceanic and Atmospheric AdministrationFonds Québécois de la Recherche sur la Nature et les Technologies
KeywordsPropagule pressurePropaguleInvasive speciesHabitatIntroduced speciesEcologyFisheryAlien speciesBiologyPopulationGeographyBiological dispersal

Abstract

fetched live from OpenAlex

Introduced species can cause economic and environmental harm. Researchers have developed risk assessment models for exotic species based on biological characteristics. However, few have quantified propagule pressure despite its relevance for establishment. Both are needed to identify invasion risk. We focused on fishes introduced via the aquarium trade, because this pathway transports thousands of species throughout the world. We developed an approach to estimate propagule pressure by (i) identifying and quantifying aquarium fishes sold, (ii) determining fish owner behavior and disposal practices, and (iii) quantifying uncertainty. We used the St. Lawrence Seaway as our model system. Only one nonestablished species ( Tanichthys albonubes , 117 per year) had the propagule pressure and environmental tolerances to likely invade this region. However, overall, more than 10 000 fishes were released annually from Montréal (Quebec, Canada) alone. The implication of the observed propagule pressures is that the aquarium trade should be a very important pathway in other warmer habitats and should be explicitly assessed. Knowledge of the numbers introduced of each species will be useful for population models to estimate the probability of establishment.

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 categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.237
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0020.002
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.054
GPT teacher head0.210
Teacher spread0.156 · 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.

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

Citations161
Published2008
Admission routes4
Has abstractyes

Explore more

Same venueCanadian Journal of Fisheries and Aquatic SciencesSame topicFish Ecology and Management StudiesFrench-language works237,207