Design of Strategy Generation System for Urban Comprehensive Disaster Prevention Planning Based on Transfer Bridge
Bibliographic record
Abstract
The rapid development of big data and artificial intelligence (AI) makes it possible to make intelligent decisions on urban comprehensive disaster prevention planning (UCDPP).Based on extension problem model and transfer bridge, this paper formulates a strategy generation system (SGS) for the UCDPP, with the aid of the AI, database technology, and extension logic analysis tools.The established system consists of three layers and multiple libraries, namely, basic database, rule base (including strong correlation rules and problem rules), question base, example database, extension transform library, and strategy base.Compared with traditional SGSs, two libraries were added to our system, i.e. knowledge base and transfer bridge library, making our system more comprehensive.Taking gas station location problem in a city as an example, the feasibility of our system was confirmed, and the visual interface of the system was illustrated in details.The research results enable urban planners to make rational decisions in the process of the UCDPP.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.007 | 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".