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Record W2135727836 · doi:10.1139/cjfas-2014-0473

Relative importance of vessel hull fouling and ballast water as transport vectors of nonindigenous species to the Canadian Arctic

2015· article· en· W2135727836 on OpenAlexafffundvenueabout
Farrah T. Chan, Hugh J. MacIsaac, Sarah A. Bailey

Bibliographic record

VenueCanadian Journal of Fisheries and Aquatic Sciences · 2015
Typearticle
Languageen
FieldEnvironmental Science
TopicMarine Ecology and Invasive Species
Canadian institutionsFisheries and Oceans CanadaUniversity of Windsor
FundersBayer CanadaTransport Canada
KeywordsBallastSpecies richnessAbundance (ecology)EcologyHullInvertebrateInvasive speciesBiologyIntroduced speciesFoulingOceanographyEnvironmental scienceFisheryGeology

Abstract

fetched live from OpenAlex

Ships’ hull fouling and ballast water are leading vectors of marine nonindigenous species globally, yet few studies have examined their magnitude in the Arctic. To determine the relative importance of these vectors in Canada’s Arctic, we collected hull and ballast water samples from 13 and 32 vessels, respectively, at Churchill, Manitoba. We compared total abundance and richness of invertebrates transported on hulls versus those in ballast water. We found that hull fouling was associated with higher total abundance and richness of nonindigenous species when compared with ballast water. Additionally, a significant positive richness–total abundance relationship for nonindigenous species for hull fouling but not for ballast water assemblages suggests that the likelihood of a high-risk (i.e., species-rich and high abundance) introduction event is greater for the former than the latter vector. The discovery of viable, widespread nonindigenous barnacles in hull samples further underscores the prominence of hull fouling over ballast water as a vector of nonindigenous species. Our study demonstrates that hull fouling is a more important vector for transfer of nonindigenous species to the Canadian Arctic than ballast water based on abundance and richness of nonindigenous species transported by the two vectors.

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)0.001
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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.618
Threshold uncertainty score0.763

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.002
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 head0.021
GPT teacher head0.201
Teacher spread0.180 · 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 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

Citations88
Published2015
Admission routes4
Has abstractyes

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