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Record W3165645765 · doi:10.1016/j.jenvman.2021.112823

Exceptions and exemptions under the ballast water management convention – Sustainable alternatives for ballast water management?

2021· article· en· W3165645765 on OpenAlexafffund
Okko Outinen, Sarah A. Bailey, Katja Broeg, Joël Chassé, Stacey A. Clarke, Rémi Daigle, Stephan Gollasch, Jenni Kakkonen, Maiju Lehtiniemi, Monika Normant, Dawson Ogilvie, Frédérique Viard

Bibliographic record

VenueJournal of Environmental Management · 2021
Typearticle
Languageen
FieldEnvironmental Science
TopicMarine Ecology and Invasive Species
Canadian institutionsBedford Institute of OceanographyDalhousie UniversityFisheries and Oceans Canada
FundersInterregEuropean Regional Development FundFisheries and Oceans CanadaInternational Council for the Exploration of the SeaMinisterstwo Edukacji i NaukiTransport Canada
KeywordsBallastConventionEnvironmental scienceBusinessStormwater managementEnvironmental planningEnvironmental resource managementEnvironmental protectionEnvironmental engineeringWater resource managementEngineeringStormwaterSurface runoffEcologyPolitical scienceBiologyLaw

Abstract

fetched live from OpenAlex

The International Convention for the Control and Management of Ships' Ballast Water and Sediments (BWM Convention) aims to mitigate the introduction risk of harmful aquatic organisms and pathogens (HAOP) via ships' ballast water and sediments. The BWM Convention has set regulations for ships to utilise exceptions and exemptions from ballast water management under specific circumstances. This study evaluated local and regional case studies to provide clarity for situations, where ships could be excepted or exempted from ballast water management without risking recipient locations to new introductions of HAOP. Ships may be excepted from ballast water management if all ballasting operations are conducted in the same location (Regulation A-3.5 of the BWM Convention). The same location case study determined whether the entire Vuosaari harbour (Helsinki, Finland) should be considered as the same location based on salinity and composition of HAOP between the two harbour terminals. The Vuosaari harbour case study revealed mismatching occurrences of HAOP between the harbour terminals, supporting the recommendation that exceptions based on the same location concept should be limited to the smallest feasible areas within a harbour. The other case studies evaluated whether ballast water exemptions could be granted for ships using two existing risk assessment (RA) methods (Joint Harmonised Procedure [JHP] and Same Risk Area [SRA]), consistent with Regulation A-4 of the BWM Convention. The JHP method compares salinity and presence of target species (TS) between donor and recipient ports to indicate the introduction risk (high or low) attributed to transferring unmanaged ballast water. The SRA method uses a biophysical model to determine whether HAOP could naturally disperse between ports, regardless of their transportation in ballast water. The results of the JHP case study for the Baltic Sea and North-East Atlantic Ocean determined that over 97% of shipping routes within these regions resulted in a high-risk indication. The one route assessed in the Gulf of Maine, North America also resulted in a high-risk outcome. The SRA assessment resulted in an overall weak connectivity between all ports assessed within the Gulf of the St. Lawrence, indicating that a SRA-based exemption would not be appropriate for the entire study area. In summary, exceptions and exemptions should not be considered as common alternatives for ballast water management. The availability of recent and detailed species occurrence data was considered the most important factor to conduct a successful and reliable RA. SRA models should include biological factors that influence larval dispersal and recruitment potential (e.g., pelagic larval duration, settlement period) to provide a more realistic estimation of natural dispersal.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.513
Threshold uncertainty score0.990

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 studies0.0010.000
Scholarly communication0.0000.001
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0100.000

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 head0.007
GPT teacher head0.206
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 teacher head, not a consensus.

Study designNot applicable
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

Citations34
Published2021
Admission routes2
Has abstractyes

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