Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.014 | 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 it