Cognitive and Computational Aspects of Marine Incident Situation Management System - the Canadian Coast Guard Use Case -
Bibliographic record
Abstract
Decision making situations in emergency management are usually characterised by participation and collaboration of multiple actors with sometimes different competences and knowledge, lacking information and/or having uncertain information. Hence, there is a need for a coherent and effective collaboration leading to shared awareness and decision support in a timely manner. This can be achieved with a collaborative incident case management platform that permits all levels of response hierarchy supporting the Concepts of Operations (CONOPS) and which complies with the measures of every agency involved bringing all responders together toward a unified efficient response.This paper considers the Canadian Coast Guard (CCG) use case in Marine Incident response system on a large scale, its inconsistencies, redundancies, gaps, and misalignments, and proposes a novel collaborative marine incident Case Management System (CMS) for shared situational awareness and decision making in marine incident management. The marine incident CMS is developed with a single approach to incident management using the Scenario Based Design (SBD) methodology and a root cause analysis to review, compare past incidents and their associated After Action Review (AAR) as well as Cognitive Task Analyses (CTAs) to validate use-case scenarios. The cognitive and computational aspects of the proposed systems are presented and discussed.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".