A Novel Earthquake Mitigation Information expert System: EMIS
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it