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Record W2113449626 · doi:10.3354/meps09231

Bacterial dynamics in ballast water during trans-oceanic voyages of bulk carriers: environmental controls

2011· article· en· W2113449626 on OpenAlexafffundabout
J. Michael Seiden, C. M. Way, RB Rivkin

Bibliographic record

VenueMarine Ecology Progress Series · 2011
Typearticle
Languageen
FieldEnvironmental Science
TopicMarine Ecology and Invasive Species
Canadian institutionsMemorial University of Newfoundland
FundersFisheries and Oceans CanadaNatural Sciences and Engineering Research Council of CanadaMemorial University of Newfoundland
KeywordsBallastPropagule pressureAbundance (ecology)Environmental scienceOceanographyPopulationEcologyBiologyGeology

Abstract

fetched live from OpenAlex

Bacterial abundance, population dynamics, and related environmental parameters were determined in ballast water during 2 trans-Pacific voyages from Japan to the west coast of Canada and 2 trans-Atlantic voyages from the Netherlands to the east coast of Canada. Water samples were collected from ballast tanks that either underwent mid-ocean exchange (MOE) or were unexchanged (control). Bacterial abundances in the MOE and control tanks were not significantly different at the end of the voyage. Moreover, abundances were not lower immediately after compared with immediately before MOE, suggesting that MOE alone is not effective in reducing ballast water bacterial abundances. Results of this study suggest that the duration of the voyage plays a critical role in determining the potential bacterial propagule pressure. Although the bacterial growth dynamics were complex, we found robust relationships and a highly significant predictive relationship between bacterial abundance and both temperature and dissolved oxygen concentrations of the ballast water (p < 0.001). This robust relationship allows the prediction of potential bacterial propagule pressure from environmental characteristics within the ballast tanks.

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 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 score0.039
Threshold uncertainty score0.962

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 science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0390.000

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 head0.005
GPT teacher head0.174
Teacher spread0.169 · 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

Citations29
Published2011
Admission routes3
Has abstractyes

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