MétaCan
Menu
Back to cohort
Record W2137184189 · doi:10.1139/f05-160

Invertebrates associated with residual ballast water and sediments of cargo-carrying ships entering the Great Lakes

2005· article· en· W2137184189 on OpenAlexvenueno aff
Ian C. Duggan, Colin DA van Overdijk, Sarah A. Bailey, Philip T. Jenkins, Helene Limén, Hugh J. MacIsaac

Bibliographic record

VenueCanadian Journal of Fisheries and Aquatic Sciences · 2005
Typearticle
Languageen
FieldEnvironmental Science
TopicMarine Ecology and Invasive Species
Canadian institutionsnot available
Fundersnot available
KeywordsBallastInvertebrateEnvironmental scienceSedimentFisheryResidualDredgingEcologyOceanographyBiologyGeology

Abstract

fetched live from OpenAlex

Most ships entering the Great Lakes carry cargo and declare “no-ballast-on board” (NOBOB) status. Approximately 250 of these vessels annually load Great Lakes’ ballast water when they offload inbound cargo and then discharge this water (which has now mixed with residual water previously present in the tanks) when they load outbound cargo. This procedure potentially allows nonindigenous species present in ballast residuals to invade the Great Lakes. We collected residual sediment, water, and associated organisms from 38 NOBOB ships entering the Great Lakes. We recorded seven established Great Lakes’ nonindigenous species, including some discovered since ballast water exchange was implemented. Occurrences of species not yet invaded indicate that this vector provides further opportunity for invasion. Collectively, NOBOB vessels appear to constitute a greater risk than ballasted vessels, as they make up a greater proportion of the traffic entering the lakes (~90%), and they do not undergo ballast exchange. Invertebrates in residual water appear to have a greater opportunity for discharge than those in sediments, although most in the water fraction have already invaded this system. Invertebrate numbers in residual freshwater ballast could be dramatically lowered if these vessels flushed with open-ocean water prior to entering the Great Lakes.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.134
Threshold uncertainty score0.879

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 studies0.0000.002
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 head0.016
GPT teacher head0.186
Teacher spread0.170 · 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

Citations98
Published2005
Admission routes1
Has abstractyes

Explore more

Same venueCanadian Journal of Fisheries and Aquatic SciencesSame topicMarine Ecology and Invasive SpeciesFrench-language works237,207