Textual risk mining for maritime situational awareness
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Bibliographic record
Abstract
In this paper, we propose an auxiliary Machine Learning (ML) and Natural Language Processing (NLP) integrated system for maritime situational awareness (MSA) operations. We bring into account a new and influential asset - human intuition and perception - to the existing semi-automated decision support systems that mostly rely on numerical data collected by electronic sensors or cameras located either directly on the vessels or in the maritime command-and-control centers. For our project, we gathered weekly textual reports spanning twelve months from the United States Worldwide Threats to Shipping Reports repository that belongs to the National Geospatial-Intelligence Agency (NGA), We considered the maritime incident reports written by human operators as a valuable and accessible unstructured textual input source in which a span of text <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> is called “risk” if it expresses one of the following kinds of vessel incidents: fired, robbed, boarded, hijacked, attacked, chased, approached, kidnapped, boarding attempted, suspiciously approached or clashed with. Our approach benefits from probability distributions of some useful features annotated based on a list of lexicons that contain expressions denoting vessel types, risks types, risk associates, maritime geographical locations, dates and times. These distributions are captured and used to anchor the span of “risks” as they are described in the textual reports. After some preprocessing steps that include tokenization, named entity extraction and part-of-speech tagging, the textual risk mining system applies a variety of sequence classification algorithms, e.g., Conditional Random Fields, Conditional Markov Models and Hidden Markov Models in order to compare the risk classification performance. Empirical results show that our NLP/ML-based system can extract variable-length risk spans from the textual reports with about 90% correctness.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it