Lickfett Disaster Portal The RESCUE Disaster Portal for Disasters and Emergency Response
Bibliographic record
Abstract
This paper describes the Disaster Portal, a multi-faceted information portal for use by citizens and emergency personnel during disasters and emergency response. This portal is the result of a collaboration between the RESCUE project at UC-Irvine and the City of Ontario, California Fire Department (OFD). The portal provides a wide range of real-time information in disaster situations, such as situation summaries, announcements, shelter information, and aggregated services such as family reunification and donation management. A first version of this portal was developed and deployed by the City of Ontario in September 2007. The current system serves as a base to develop and refine results from several areas of research which are being incorporated into the existing system to provide additional or advanced capabilities. We provide an overview of the portal’s capabilities, followed by technical details on the system architecture and implementation. We describe the experience of deployment of the system in the recent California wildfires. Finally we describe work in progress on several advanced capabilities.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.113 | 0.043 |
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".