A Novel Earthquake Mitigation Information expert System: EMIS
Bibliographic record
Abstract
We present an expert system profile (EMIS) that serves as an information system for earthquake mitigation purpose. Knowledge-based systems, also called expert systems, are problem solving systems that use a knowledge base (KB) as one of their central components. Crucial phases for building an effective knowledge-base include data collection, knowledge acquisition and knowledge representation. In this paper, we first identify two main reasons for lack of effectiveness of available disaster management systems namely, lack of real data of the disaster and inability to appropriately extract as well as represent knowledge gained from such data in the disaster management system. Most disaster management systems assume that knowledge is provided by the experts, which may not always be possible. We explore data mining techniques to automate this process of knowledge acquisition. Further, such knowledge about natural catastrophes is highly uncertain due to the very nature of disasters. We make an attempt to manage and reason under uncertainty. Experimentation with real-life dataset show how our system allows users acquire information about earthquakes in the chosen area and mitigation steps required to be taken before, during and after such disasters
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.003 |
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".