Risk and Emergency Management System to Mitigate Disasters
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
Disasters lead to many violent strikes in terms of material, human and humanitarian losses, and destruction of property and infrastructure of the state. The term "emergency management" refers to a diverse range of activities. The primary responsibility for disaster response rests with the government at all levels (Peiris P. S. H. 2020, Opadey. 2021). Disaster management is still limited in Iraq, as long as necessary are in place to warn of these disasters. Therefore, we will use in this prepare the Remote Sensing (RS) and Geographic Information System (GIS) to mitigate disasters effects and focus on sandstorm, landslides also study prediction and prepare the prediction and GIS earthquake of east of Iraq. The Risk management systems are useful and effective tools for disaster management in Iraq and by using database on expected disaster for future. The future is to forecast, reduce damage, and assess the severity of these disasters. This essay will list emergency management initiatives and explain how geographic information systems can be used (GIS) and Remote System (RS) technologies play a critically important role in mitigating 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 | 0.039 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.002 |
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