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Record W4281568165 · doi:10.1016/j.jenvman.2022.115300

Assessing the performance of four indicative analysis devices for ballast water compliance monitoring, considering organisms in the size range ≥10 to <50 μm

2022· article· en· W4281568165 on OpenAlexaffabout
Oscar Casas‐Monroy, Jocelyn Kydd, R. Rozon, Sarah A. Bailey

Bibliographic record

VenueJournal of Environmental Management · 2022
Typearticle
Languageen
FieldEnvironmental Science
TopicMarine Ecology and Invasive Species
Canadian institutionsFisheries and Oceans Canada
Fundersnot available
KeywordsBallastEnvironmental scienceEnvironmental engineeringEcologyBiology

Abstract

fetched live from OpenAlex

To minimize the global transfer of harmful aquatic organisms and pathogens, the International Maritime Organization (IMO) has introduced the standard in Regulation D-2 to limit the number of viable organisms in ballast water discharged by ships. To meet the standard, many ships are installing ballast water management systems. Concurrently, regulators are looking for indicative analysis devices able to assess compliance with Regulation D-2, producing rapid, accurate and reliable results while being easy to operate. The purpose of this research is to compare four indicative analysis devices against detailed microscopy for measuring the size class of organisms ≥10 to <50 μm in minimum dimension (e.g., phytoplankton, including autotrophs, heterotrophs or mixotrophs), using field and laboratory tests. Comparisons were conducted on (treated) ballast water discharge samples collected across Canada during three consecutive years (2017–2019). During seven tests in 2019, paired ballast water uptake samples were also obtained, facilitating measurements before and after treatment was applied. Indicative analysis devices also were challenged with natural environmental samples containing different organism abundance levels, ranging from low (nominally <10 cells mL−1) to high (nominally >150 cells mL−1) during laboratory tests. While the indicative analysis devices examined during this research produced numeric estimates having weak correlations with the standard reference method, categorical outcomes (above/below the D-2 standard) had high agreement (89% or better) when assessing ballast water samples, but lower agreement (67% or poorer) during laboratory tests. There was a relatively high rate of false negative results measured by all devices during laboratory tests. Results provided by indicative analysis devices had higher uncertainty when organism abundances in ballast samples are below and close to the D-2 standards.

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)0.003
metaresearch head score (Gemma)0.009
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
Metaresearch0.0030.009
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0020.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.001

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 head0.027
GPT teacher head0.259
Teacher spread0.232 · 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

Citations10
Published2022
Admission routes2
Has abstractyes

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