Mapping Gray Maritime Networks
Bibliographic record
Abstract
Abstract This research focused on the identification and tracking of subgroups of vessels of interest, owners, operators, ports, cargoes, and specific activities associated with artificial reef enhancement and construction in the South China Sea. Historical automated information system (AIS) tracks and current maritime databases were used to develop sociogram depictions of the gray (licit but only partially transparent) maritime network that connects these nodes (ships, events, organizations, ports, activities). Social network matrices were dynamically updated by open source databases to provide insights into real-time awareness and tracking for operational purposes. The maritime network data set was populated by, and dynamically updated through, the integration of unclassified data using algorithms developed as part of the research. Longitudinal topographic metrics – average degree, average clustering coefficient, and centralization – were used to analyze the multi-mode (e.g., ship to ship, ship to owners/operators, owner/operators to owner/operators, ships to locations) relationships within the gray maritime network. Additionally, the network of ports and reefs in the area of operations was mapped and insights were gained by leveraging directed centrality measures – hubs and authorities – connecting them.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".