A Multi-Function Disaster Decision Support System Based on Multi-Source Dynamic Data
Bibliographic record
Abstract
Disasters are unpredictable. However, occurrences follow a specific time sequence. Disaster management encompasses routine disaster reduction, pre-disaster preparation, mid-disaster response, post-disaster recovery, time management and allocating routine tasks over an extended period, and emergency response during highly stressful periods. Various response organizations rely on effective “integrated disaster management” to react to situations at different periods in time. In addition to making personnel and organization adjustments at different times, integration also requires systems for effective and fast communication and for providing first-hand supporting information to responders for data, manpower, organization, and resource integration. Based on design science theory, disaster decision support systems integrate internal and external data through (1) confirming problems and motivations, (2) defining solution objectives, (3) designing and developing a solution, (4) presenting the solution, (5) evaluating the solution, and (6) communicating protocols, and then consolidating the data into graphical or visual platforms and systems. These systems not only contain disaster prevention information, provide pre-disaster emergency response warnings, allocate supporting resources for mid-disaster response, evaluate the scale of disasters, and formulate response plans, but also simulate various disaster situations and scenarios during disaster-free periods for training and education purposes.
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.
How this classification was reachedexpand
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 | 0.002 | 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 | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".