Optimized Ballast Water Exchange Management for Bulk Carriers
Bibliographic record
Abstract
Many port states, such as New Zealand, U.S.A., Australia, and Canada, have strict regulations to prevent arriving ships from discharging polluted ballast water that contains harmful aquatic organisms and pathogens. They are notified that transfer of polluted ballast water can cause serious injury to public health and damage to property and environment. For this reason, ballast exchange in deep sea is perceived as the most effective method of emptying ballast water. The ballast management plan contains the effective exchange method, ballast system, and safety considerations. In this study, we pursued both nautical engineering analysis and optimization of the algorithm, in order to generate the sequence of stability and rapidity. A heuristic algorithm was chosen on the basis of optimality and applicability to a sequential exchange problem. We have built an optimized algorithm for the automatic exchange of ballast water, by redefining core elements of the A algorithm, such as node, operator, and evaluation function. The final version of the optimized algorithm has been applied to existing bulk carrier, and the performance of the algorithm has been successfully verified.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
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".