Do Ballast Water Management Systems Reduce Phytoplankton Introductions to Canadian Waters?
Bibliographic record
Abstract
Global coastal aquatic ecosystems are negatively impacted by the introduction of harmful aquatic species through the discharge of ships’ ballast water. To reduce discharges of harmful aquatic organisms and pathogens, such as toxic phytoplankton species, ships are now transitioning to the use of ballast water management systems (BWMS) instead of ballast water exchange (BWE). This study examines the abundance and diversity of phytoplankton in ballast water managed by BWMS (or a combination of both BWE + BWMS) in comparison to those in ballast water managed by BWE alone (collected from ships arriving to Canada’s Pacific coast in 2017–2018 and 2008, respectively). The abundance and diversity of phytoplankton species were also examined in relation to key variables such as ballast water salinity and ballast water age. Total abundance of phytoplankton was significantly lower in preserved samples managed by either a BWMS or BWE + BWMS compared to BWE alone. Abundances in preserved samples were higher than observed in fresh (unpreserved) samples at the time of collection, with all samples managed by a BWMS meeting international limits for the number of viable organisms ≥10 and <50 μm in minimum dimension (based on six 1-mL live counts). While there was no apparent influence of factors such as treatment type [e.g., ultraviolet (UV) or chlorine], presence of filtration, ballast water salinity, ballast water age, nor location of last ballast water uptake on phytoplankton abundances in preserved samples, power to detect differences may be limited by sample size. Ballast water managed by BWMS also tended to have lower abundances of harmful phytoplankton species, although the difference was not statistically significant – additional research into the community composition of live cells in fresh samples could be valuable to discriminate the risk associated with phytoplankton surviving ballast water treatment.
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 imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
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".