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Record W1976819141 · doi:10.1139/f09-180

Domestic ballast operations on the Great Lakes: potential importance of Lakers as a vector for introduction and spread of nonindigenous species

2010· article· en· W1976819141 on OpenAlexaffvenue
Michael P. Rup, Sarah A. Bailey, Chris Wiley, Mark S. Minton, A. Whitman Miller, Gregory M. Ruiz, Hugh J. MacIsaac

Bibliographic record

VenueCanadian Journal of Fisheries and Aquatic Sciences · 2010
Typearticle
Languageen
FieldEnvironmental Science
TopicMarine Ecology and Invasive Species
Canadian institutionsUniversity of WindsorTransport CanadaFisheries and Oceans Canada
Fundersnot available
KeywordsBallastInvasive speciesEnvironmental scienceIntroduced speciesFisheryEcologyOceanographyGeographyBiologyGeology

Abstract

fetched live from OpenAlex

Ballast water is recognized globally as a major vector of aquatic nonindigenous species (NIS) introductions; domestic ballast water transfers, however, have generally been considered low risk in North America. We characterize ballast operations of domestic ships in the Great Lakes – St. Lawrence River system (Lakers) during 2005–2007 to examine the risk of primary and secondary introductions associated with ballast water transfers over short distances. Results indicate that Lakers transported at least 68 million tonnes of ballast water annually. Approximately 71% of ballast water transfers were interregional, with net movement being from lower to upper lakes. A small proportion of ballast water discharged in the Great Lakes (<1%) originated from ports in the St. Lawrence River that may serve as sources for new NIS. These results indicate that domestic ballast water transfers may contribute to NIS introductions and are likely the most important ballast-mediated pathway of secondary spread within the Great Lakes. Future efforts to reduce invasion impacts should consider both primary and secondary introduction mechanisms.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.177
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
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)0.0020.000

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 head0.009
GPT teacher head0.202
Teacher spread0.193 · 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

Citations50
Published2010
Admission routes2
Has abstractyes

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