Synthesizing data-to-wisdom hierarchy for developing smart systems
Bibliographic record
Abstract
Smart systems are defined as miniaturized devices that incorporate functions of sensing, actuation, control, and adaptation. They are capable of describing and analyzing a situation, and taking decisions based on the available data in a predictive or adaptive manner, thereby performing smart (intelligent) actions. In order to effectively manage any situation confronted by it, the system components and devices must work in consort with each other. A smart system must interface, interact and communicate with users, physical devices which may themselves be embedded in other smart systems, and their environment. Such systems have to deal with enormous amount of data and information. To cope with the heterogeneity of data and information and synthesize them in any situation the system must have sufficient knowledge on the semantics of information domains, and manage well-defined policies that will enable it to safely and securely operate in its life cycle. This paper explains how the introduction of context-awareness capabilities in Data, Information, Knowledge, and Wisdom (DIFK) hierarchy can serve as the basis to construct Wisdom-Intelligence-Creativity-Smart System (WICSS) model, which in turn can be a beacon light for validating the design and implementation of Smart Systems.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.005 | 0.008 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".