Customizing Urban Pattern through an Agent-Based Approach
Bibliographic record
Abstract
This paper discusses the 3D space customization of design concepts within self-generated sculpture as an instigator for design of urban pattern. Appropriating from the concept of computer fuzzy logic, fuzzy design prods serve as exemplars of naturally occurring swarm behaviors. The hybridization of design through the ‘mistake' and the different material vocabularies serve as departure points for the conceptualization of image breeding in 2D and for 3D grouping within urban pattern. Additive and eroding material processes spawn rule-based agent behaviors that assist the designers/artists to conceive and to enhance appearance and place. In an iterative process, swarm entities physically augment forms in an organic manner. The designer becomes the voyeur of their own creative input as swarm behaviors influence the placement and grouping of architecture/sculpture within the urban pattern of cities. In particular, this paper focuses on the agent-based approach whereby swarm behavior classifies residential, commercial and green spaces within urbanized areas.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".