Fusion of Multi-level Granular Data using Evidence Theory
Bibliographic record
Abstract
Abstract Complex systems are often composed of multiple subsystems arranged in a multi-level hierarchical structure. Therefore, dedicated methods suitable for determining the local states of such arranged components and the global state of the system are important. Subsystems’ dependence on each other and unreliable inputs make forming definitions of the subsystems’ states challenging. As a result, the state descriptions could contain imprecise terms expressed as data granules. Moreover, the granules ’conceal’ partial information and unclar-ity on what values of inputs and states of other subsystems are required to determine the local and global states of the system. In this paper, we introduce and describe a novel approach to determining states – local and global – of complex multi-component systems of hierarchical architecture. We use elements of Evidence Theory and adopt a newly developed method that satisfies uncertain targets to assess the system’s states. In our case, the uncertain targets are definitions of subsystems’ states. This process is performed in stages following the system’s architecture. The inputs are used at the lowest levels of the hierarchy, and the processing at higher levels uses the results of lower-level computations. Due to the imprecision of inputs and definitions of subsystems’ states, the proposed approach deals with multiple sources of uncertainty in determining the states. The origins of imprecision are categorized into: 1) uncertainty and ambiguity associated with measured quantities as inputs to subsystems, 2) degrees of imprecision in determining states of subsystems calculated based on the states of other subsystems, and 3) imprecision and incomplete knowledge included in the statements defining subsystems’ states.
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.
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 | 0.005 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.009 | 0.006 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".