METIS: Dependable Cooperative Systems for Public Safety:
Bibliographic record
Abstract
Much, if not most, information needed to assess a crisis situation originates these days from cooperative sources such as the Internet and social networks. Public safety authorities face the challenge to compile this information of uncertain origin and quality in their situation understanding and response planning. Time matters: the integration of uncertain information needs to be done in a fast, goal-driven and ad-hoc manner.Such situation understanding requires system support in the form of a dependable and cooperative system-of-systems: able to adapt semi-automatically to new situations and to improve the value of the information using built-in reasoning and awareness techniques. The METIS project researches such system support for public safety as a collaborative project of Dutch universities, knowledge institutes, and industry, using the maritime domain as case study. The METIS goal stretches the scope of system engineering, as the main requirements of ad-hoc adaptation and dependability contradict each other.In this paper, we describe the METIS information architecture and highlight our four major research lines: (i) System architectures beneficial for dependability and adaptability; (ii) Application and system dependability ensured by embedded awareness; (iii) Ad-hoc system adaptability and goal-driven system reconfiguration; (iv) Integration and semantic alignment of various (natural language) information sources
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".