Past, present, and future of the satellite-based automatic identification system: areas of applications (2004–2016)
Bibliographic record
Abstract
In 2016, the world shipping fleet grew by 3.5%. Even if the annual growth rate remains at its lowest since 2013, the global situation is still in overcapacity (UNCTAD 2016 ). Ninety percent of global trade, by volume, is done by sea. Monitoring this fleet helps with vessel navigation, informing to help avoid critical situations such as collisions, accidents leading to oil pollution, grounding, or ships in distress, but also because traffic management in congested areas is essential. For system wide management, in regions such as MPAs (marine protected areas), conservation is the key factor, and movements can be monitored and analyzed in order to determine illegal or suspicious activities, or in order to limit and/or divert traffic, to mitigate the risks to species subject to protection. It is among these efforts that the automatic identification system (AIS) can play a key role. Since 2004, this VHF transceiver-based reporting system, imposed by the International Maritime Organization (IMO), has shifted from a traditional vessel identification device to a tool used in a wide variety of applications. The most common uses are safety and security; these issues are quite visible in the media and may touch more people on a global scale (e.g., piracy, oil spills). Over the years, AIS has become, especially with the emergence of the satellite-based capture of the signal in 2011, a widely used tool for developing applications such as fisheries monitoring, marine conservation, air pollution forecasting and modeling, ballast water monitoring, invasive species transport, and many more. In this paper, we propose to review the peer-reviewed publications related to the uses and applications of the AIS.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".