Interactive Placemaking - Prototype of an Intelligent Urban Building Infrastructure for Critical Borderlands / Kinmen
Bibliographic record
Abstract
Dependable capabilities to counter wicked tasks are proactively needed for borderland placemaking. Initiated recently, in Xiamen, a hub airport is being constructed at Xiangan district’s Dadeng islet, which was taken after the inner war from the outlying borderland of Taiwan - Kinmen. To promote current interactive urbanizing, holistic pervasiveness, and/or resource chaining, a sensor-information infrastructure with availability, reliability, and maintainability features through artificial intelligence -i.e., the plural surveillances, similar in sensing through creatures’ eyes, ears, and nostrils- is prototyped. By plural detection along traffic paths, such dedicated short-range communication networks, evidenced with terrorist uncertainty, can help promote trust, inclusive accessibility, mobile services, and interactive measures, with fault tolerance, Hamiltonian (a mathematical order), connectivity, scalability, electromagnetic interference countering, and reliable accuracy. More flexibility can be justified for the emerging pervasive logistic and security management demand by further applying other network types. The cube-connected cycle is prototyped for the probe application along waterways and in rural areas. The prototyped spider-web network can be resiliently integrated with shorelines and interior paths and configured together into a featured urban building context, radial-ring, whose sustainability is culturally resonated in world heritage, neighboring Tulous; help promote peace developing and evoke economic interactions, globally.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.008 | 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".