Metis : an integrated reference architecture for addressing uncertainty in decision-support systems:
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
Deliver âactionableâ intelligence instead of just raw information â this is what the Metis research project pursues for supporting operational work in domains characterized by constantly evolving situations with a diversity of entities, complex interactions and high-level uncertainty in the information gathered. Operating effectively in such domains requires robust, on-the-fly and context-based information reasoning, which goes beyond human capabilities. In this paper a real-time reference architecture is presented employing and integrating several state-of-the-art computing technologies for automated and consolidated âsituational understandingâ. In particular, outlined are the innovative components (i) for fusing of and reasoning on uncertain information based on probabilistic logic and (ii) for a complementary interactive visualization disclosing the systemâs line of reasoning inferred from the domain model and provided evidence. The architecture has been realized as a fully demonstrable proof of concept and its applied value is illustrated in a number of real and fictive cases from the domain of maritime safety and security.
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 | 0.001 | 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 | 0.001 | 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