Functional and Spatial System Model for City Infrastructure Systems: A City.Net <scp>IES</scp> Case Study
Bibliographic record
Abstract
ABSTRACT City infrastructure systems have distinct functions but are not isolated from one another, with interactions existing between these systems. Modeling these systems requires a focus on the system functions and interdependencies. Most models focus on system failures rather than the unexpected effects of design decisions in these systems. This paper presents a functional and spatial modeling framework suited for the representation of city infrastructure systems. This framework comprises a systematic process for breaking down the system into fundamental components and defining the relations between the system components. In addition, the spatial feature of the framework facilitates the synthesis, analysis, and evaluation of infrastructures based on their geographical locations and spatial orientations. This system modeling approach is used to design an Integrated Energy System ( IES ) model in order to exhibit the features of this framework. The IES consists of standard energy system estimation techniques and tools such as MATPOWER for load flow analysis, and is also used to execute a city case study. As a result, the advantages of the functional and spatial framework for modeling city infrastructures are presented.
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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 | 0.001 | 0.001 |
| 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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".