E-CITY KNOWWARE: KNOWLEDGE MIDDLEWARE FOR COORDINATED MANAGEMENT OF SUSTAINABLE CITIES
Bibliographic record
Abstract
The realization of e-city is a necessary component for achieving the green city. This paper outlines a vision for an e-city platform that is based on knowledge brokerage in the green city. The proposed platform will be a venue for creating dynamic virtual organizations to harness collective intelligence of knowledge hubs to analyze and manage sustainability knowledge in urban areas. Knowledge assets of participating organizations will be presented in three dimensions: process structures, human profile and software systems. These three facets of knowledge will be accessible and viewable through a self-describing mechanism. Cities can post their geospatial and real-time data on the net. Relevant environmental and energy-use data will be extracted using topic maps and data extraction services. Local decision makers can synchronize work processes (from participating hubs) to create an integrated workflow for a new ad hoc virtual organization to collaboratively analyze the multifaceted nature of sustainable decision making. An e-city platform is envisioned in this paper that will be realized through intelligent, agent-like, domain-specific middleware (KnowWare). Through triangulation between people, software and processes, these KnowWare will discover, negotiate, integrate, reason and communicate knowledge (related to energy and environment) from across organizations to the right person at the right time. KnowWare is fundamentally, a portal of social semantic services that resides on a cloud computing infrastructure. Knowware exploits thee main tools: 1) existing ontologies to represent knowledge in a semantic manner, 2) topic maps to profile sources of knowledge and match these to the complex needs of sustainability analysis, 3) domain-specific middleware for knowledge integration and reasoning.
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How this classification was reachedexpand
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 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".