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Record W2318343536 · doi:10.2166/wqrjc.2013.117

Effectiveness and potential environmental impact of a yeast-based deoxygenation process for treating ship ballast waters

2013· article· en· W2318343536 on OpenAlexaff
Yves de Lafontaine, Yildiz Chambers, Simon-Pierre Despatie, Christian Gagnon, C. Blaise

Bibliographic record

VenueWater Quality Research Journal · 2013
Typearticle
Languageen
FieldEnvironmental Science
TopicMarine Ecology and Invasive Species
Canadian institutionsEnvironment and Climate Change Canada
Fundersnot available
KeywordsBallastEnvironmental scienceAnoxic watersEnvironmental chemistryDeoxygenationAquatic ecosystemBioassayEnvironmental engineeringChemistryBiologyEcology

Abstract

fetched live from OpenAlex

We assessed the effectiveness and potential environmental impact of a yeast-based deoxygenation process considered for treating ship ballast waters to reduce the risk of aquatic species introduction. Laboratory experiments were conducted to test three treatment concentrations (0.33%, 0.67% and 1.0% v v−1) at five temperatures (4, 10, 15, 20 and 25 °C) in both fresh- and saltwater, with and without mixing. Complete anoxia (<0.3 mg L−1) was achieved in all experiments, and there were no significant differences in effectiveness between fresh- and saltwater or between mixing levels. Time to hypoxia was inversely related to temperature, ranging from half a day at 25 °C to nearly 7 days at 4–5 °C. The process can quickly generate and maintain anoxic conditions over a long enough period of time to effectively eliminate a wide variety of aquatic organisms. Results of six bioassays indicated that treated waters were not toxic at the end of experiments and would not pose a toxic risk to natural receiving waters. Increased concentrations of ammonia, organic carbon and particulate matter resulting from yeast production in treated waters may cause some potential adverse environmental effects. The practicality of implementing this process for treating ballast water in ships is discussed.

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)0.003
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.420
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.000

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 head0.046
GPT teacher head0.362
Teacher spread0.316 · 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

Citations7
Published2013
Admission routes1
Has abstractyes

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