Current Limitations and Emerging Trends in Real-Time Mapping of Natural Disaters and the Emergence of Disaster Dashboards for Communicating Risk
Bibliographic record
Abstract
The frequency and magnitude of natural disasters, especially floods, has been increasing in recent years and climate is expected to exacerbate these events. The increasing availability and number of satellites with continually improving spatial and temporal resolutions can provide efficient data sources to help with real-time disaster relief efforts from hazard assessment to rescue operations. Analyzing these volumes of data requires highly efficient algorithms that can produce fast and accurate results across the complex landscape. Artificial Intelligence (AI) provides an opportunity for repeatable, timely and reliable data processing. Once mapped, getting the spatial information to the users/decision-makers rapidly and in a format that is easily understandable and accessible has been a challenge. New methods of data sharing are emerging and gaining popularity. The widespread stability and availability of the internet has led to a surge in direct data access via Application Programming Interface (API) and interactive dashboards. Since the declaration of a global pandemic, there has been dozens of COVID-19 dashboards developed for tracking new cases. The overwhelming response to these dashboards has led to growth in the development of dashboards focused on assisting the emergency management community and first responders, as these dashboards can provide a common operating picture for multi-users operating from different locations.
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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 | 0.023 | 0.051 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.005 | 0.008 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.010 | 0.020 |
| Open science | 0.012 | 0.005 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.025 | 0.010 |
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".