Bibliographic record
Abstract
Maritime Information Warfare (MIW) provides a unifying concept for the integration, within naval operations, of information; command and control (C2); intelligence, surveillance and reconnaissance (ISR); electronic warfare (EW); and cyber systems. MIW leverages the plethora of socio-technical networks, sensors and information sources (e.g. terrestrial, space based, open sources) to support the development of a multilayered, multi-domain operational maritime picture. However, modern navies are relentlessly challenged by the rapid changes in communications; sensors; signal processing; information management; and, imaging technologies. To illustrate the MIW R&D challenges and opportunities facing the Royal Canadian Navy (RCN), this presentation highlights some of the concepts and technologies being explored within the current research program. This will include new sensors and information management technologies being developed within Defence R&D Canada. This research will be exploited to ensure optimal operational and tactical level decisions for both independent and coalition maritime operations in domestic and global theatres; and, support Command's ability to maintain both effective and technologically relevant Command Decision Support and Control.
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.
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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.005 | 0.009 |
| Scholarly communication | 0.012 | 0.010 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".