A review of ballast water management practices and challenges in harsh and arctic environments
Bibliographic record
Abstract
Ballast water is carried by cruise ships, large tankers, and bulk cargo carriers to acquire the optimum operating depth of the propeller and to maintain maneuverability and stability. Recently, ballast water has been recognized as wastewater that is responsible for ocean pollution due to the worldwide transfer of non-indigenous species, pathogenic bacteria, and other pollutants via ballast water discharge. This poses serious environmental, ecological, and economic threats to both coastal communities and the marine environment. To address these negative impacts and concerns, the International Maritime Organization (IMO) has codified and adopted a series of guidelines to minimize pollution and adverse effects caused by ballast water. A number of treatment technologies have been developed and applied in field practices to remove solids, particulates, organic pollutants, and organisms from ballast water, showing certain advantages and limitations. Many other management practices, such as ballast water exchange (BWE), shipping routes optimization, treatment process modeling, and risk assessment are in high demand to aid onboard treatment systems. However, knowledge and technical gaps still exist regarding the implementation of ballast water management practices especially in the context of arctic and harsh environments under changing climatic conditions. Records indicate that most coastal regions in the north have been invaded by unwanted species via ballast water discharge in the past decades. The North Atlantic and the Arctic Oceans have much colder climates and more extreme weather conditions than low latitudes. The discharge of untreated or less treated ballast water could cause much more severe damage to the local environment and hence pose higher risks to ecosystems and even human health, particularly in the context of climate change. Based on a comprehensive literature review, this study proposed a risk-based fuzzy–stochastic–interval programming decision support system to help eliminate environmental, ecological, as well as health threats from the discharge of ballast water, particularly in the north where weather, space, timing, maintenance, and cost are major concerns.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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; both teacher heads agree on what is shown here.
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".