Intelligent Decision Support for Emergency Responses
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
With a coastline touching upon the Pacific and Atlantic Oceans, the Great Lakes and the Arctic Sea, the Canadian MSOCs are faced with a daunting task. They are responsible for both routine duties, including patrolling coastal areas and collecting satellite data, as well as critical missions, such as emergency response and crime intervention. Both kinds of mission require the fusion of data from a variety of sources and the orchestration of myriad heterogeneous resources over great physical distances. They must deal with uncertainty, both in terms of what can be known and also in the outcomes of actions, and must interact with an environment prone to dynamic change. We present the architecture and core mechanisms of a decision support system for marine safety and security operations (Glasser, Jackson, Araghi, When and Shahir, 2010). The goal of this system is to enhance complex command and control tasks by improving situational awareness and automating task assignments. This system concept includes adaptive information fusion techniques integrated with decentralized control mechanisms for dynamic resource configuration management and task execution management under uncertainty. Autonomously operating agents employ collaboration and coordination to collectively form an intelligent decision support system.
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 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.080 | 0.008 |
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