Epidemic Attack on the Aircraft Carrier Theodore Roosevelt: Bridging the Gaps in Emergency Management
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
This paper advocates for causal models of the emergency management cycle (EMC) for decision support in epidemic or pandemic scenarios. The model capability is demonstrated for the case of the COVID-19 attack at the NATO flagship USS Theodore Roosevelt in early 2020. Computational intelligence is a reasonable approach for dealing with uncertainties such as low reliability of information and source credibility. The proposed EMC causal models enable the development of countermeasures for epidemiological attacks using the notion of gaps in the four EMC phases: mitigation, preparedness, response, and recovery. In particular, the EMC problem can be formulated and formalized as bridging the identified technology–society gap, e.g., mitigation of risks and biases; and machine reasoning can be incorporated at any level of the EMC decision-making. Using available real-world data on the USS Theodore Roosevelt outbreak, we show how machine reasoning mechanisms can help the captain to make more reliable decisions in critical epidemiological situations.
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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 | 0.006 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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