Aquatic invasive species vector management: challenges and practical solutions on the eve of new global shipping regulations
Bibliographic record
Abstract
A new world standard for ballast water management (IMO-D2) will be enforced commencing September 2017. This thesis aims to achieve required final population abundances for target organisms. In chapter 2, I tested synergy effects with two ballast water treatments (chlorination and ballast water exchange). Chapter 3 evaluated the number and volume of samples required to achieve defined error rates. Chapter 4 estimated potential production and exposure to disinfection by-products that may occur when chlorine-treating ballast water. Shipboard trials were carried out en route from Canada to Brazil with sampling carried out using a multiport ballast-tank sampling installation designed for these experiments, followed by statistical modeling and simulation for accuracy determination. Bench experiments for by-product formation were carried out with water samples collected from the same origin ports and a ballast tank to mimic water salinity and natural organic matter content. By-products were analyzed over time to determine potential exposure of vessel personnel. Combined treatment performed equal or better than each treatment alone. Synergistic effects were found for Escherichia coli resulting in greatest reductions when treatments were combined. Antagonistic effects (i.e. less than additive) were detected for phytoplankton and coliform bacteria, possibly due to replenishment of individuals after ballast water exchange. Synergistic effects could not be assessed for zooplankton due to complete elimination of viable individuals in all chlorine treatments. Multiport sampling reduced variability from within-tank aggregation. As volume and replicate number increased, error rates decreased. The best tradeoff for accuracy, precision and practicality was obtained using 1m3 ballast samples. Concerns for potential exposure to chemical treatment by-products for vessel personnel were justified, as single-pulse dosing can lead to significant production of harmful trihalomethane by-products, particularly in brackish ballast water with greater natural organic content, but also for marine and freshwater ballast supplemented with organic content. Freshwater chemical by-product levels were lowest for all treatments examined. Meeting performance-based ballast water effluent standards starting in 2017 will be challenging. My thesis demonstrates that sample sizes for effluent compliance testing should be substantial (1 m3), and that combinations of treatments may offer the greatest opportunities for reducing target organism abundances to values below permissible thresholds.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.020 | 0.003 |
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 source (direct Gemma or distilled Codex), 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".