An Expert System for Local Flood Response Coordination and Training
Bibliographic record
Abstract
Flood response is an essential component of flood management to rescue people, reduce property loss, and limit the impact to the environment. Effective flood response depends on a sound coordination structure with unified responsibilities, smooth communications, and scalable response plans. An efficient coordination system, including command and management structures, is built on a thorough understanding of the responsibilities and actions of each role for delivering the response core capabilities. Collecting, sharing, using, and handling the knowledge require great efforts in knowledge management. To further enhance such efforts, an expert system for local flood response coordination and training (LFRS) was developed and introduced in this paper. LFRS can help emergency managers construct scalable, flexible, and adaptable coordination structures and support educating flood response entities, such as individuals, communities, nongovernmental organizations, private sector entities, and local governments. The output of the prototype expert system contains two CSV formatted reports as well as prompt screens. The operational structure report hierarchically depicts the crisscross linkages among all responders, their primary functions, and contact information. Another report summarizes the responsibilities and actions of a certain role of flood responders from commanders to individuals.
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 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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.034 | 0.011 |
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".