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First evaluation of ballast water management systems on operational ships for minimizing introductions of nonindigenous zooplankton

2022· article· en· W4289313297 on OpenAlexafffund
Sarah A. Bailey, Torben Brydges, Oscar Casas‐Monroy, Jocelyn Kydd, Robert Dallas Linley, R. Rozon, John A. Darling

Bibliographic record

VenueMarine Pollution Bulletin · 2022
Typearticle
Languageen
FieldEnvironmental Science
TopicMarine Ecology and Invasive Species
Canadian institutionsFisheries and Oceans Canada
FundersOffice of Naval ResearchFisheries and Oceans CanadaInternational Council for the Exploration of the SeaUniversity of WindsorTransport CanadaU.S. Environmental Protection Agency
KeywordsBallastZooplanktonEnvironmental scienceOceanographyFisheryMarine engineeringEnvironmental engineeringEcologyBiologyEngineeringGeology

Abstract

fetched live from OpenAlex

Ballast water is a leading pathway for the global introduction of aquatic nonindigenous species. Most international ships are expected to install ballast water management systems (BWMS) by 2024 to treat ballast water before release. This study examines if ballast water discharges managed by BWMS are meeting standards for organisms ≥50 μm in minimum dimension (i.e., <10 organisms per m 3 ; typically zooplankton). Representative samples of ballast water were collected from 29 ships (using 14 different BWMS) arriving to Canada during 2017–2018. Fourteen samples (48 %) had zooplankton concentrations clearly exceeding the standard (ranging from 18 to 3822 organisms per m 3 ). Nonetheless, compared to earlier management strategies, BWMS appear to reduce the frequency of high-risk introduction events. BWMS filter mesh size was an important predictor of zooplankton concentration following treatment. Greater rates of compliance may be achieved as ship crews gain experience with operation and maintenance of BWMS.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.012
Threshold uncertainty score0.024

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0070.001

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 head0.019
GPT teacher head0.219
Teacher spread0.200 · 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 source (direct Gemma or distilled Codex), 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

Citations43
Published2022
Admission routes2
Has abstractyes

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