Weather-aware maritime patrolling with dynamic covering and emission control
Bibliographic record
Abstract
Maritime patrolling operations consider multiple factors, such as the location and movement of possible accident points, participation and routes of vessels of opportunity (VOO), weather conditions, and emissions. A key objective is to have the Coast Guard vessels cover as many accident points as possible. They will do so while minimizing the distance to uncovered accident points and reducing environmental impacts. Other considerations include coordinating VOO, preserving underwater life, and recognizing prohibited areas. This study introduces a novel approach to integrate “Search and Rescue” coverage and maritime transportation activities in a dynamic setting. The primary goals are to maximize coverage, minimize distances to uncovered accident points, and reduce environmental impacts. Because of the complexity of this multi-objective problem, the research employs an enhanced multi-objective particle swarm optimization technique with adaptive operator selection. The findings enhance our understanding of Coast Guard vessel routing and scheduling and establish a comprehensive framework to address meteorological conditions , coverage efficiency, and sustainability in patrolling operations. This study contributes to the evolution of proactive and effective strategies for maritime safety, emphasizing the necessity of integrating environmental considerations into patrolling optimization.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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) | 0.001 | 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".