An Information System for Real-Time Critical Infrastructure Damage Assessment Based on Crowdsourcing Method: A Case Study in Fort McMurray
Bibliographic record
Abstract
Nowadays, regular functions of modern societies strictly depend on critical infrastructures (CIs). Hence, the disruption and suspension of CIs service can result in serious consequences to the economy and citizens’ life. Natural disasters, as one of the main causes of CIs disruption, present an increase trend of occurrence in recent decades all around the world. Therefore, rapid and reliable damage assessment and estimation are in need to support the crisis management center (CMC) to make reasonable decisions. The goal of this paper is to develop an information system for real-time CIs damage assessment based on the crowdsourcing method. The proposed system mainly consists of a geodatabase and a user interface. The interface functionalizes for CIs damage data collection from the public while the geodatabase is the data warehouse. Then, the CMC can refer to the database to conduct real-time CIs damage assessment and make corresponding decisions more efficiently. To validate the system, the authors performed a case study of wildfire disaster. The study show that the system can make the rapid damage assessment possible. Limitations and further improvement of the current study were discussed.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".