Future disaster scenario using big data: A case study of extreme cold wave
Bibliographic record
Abstract
The ability to predict the future was considered a very important factor for humanity since long ago.Future prediction, which was non-scientific, took on significant developments with the advancement of science and technology.Nevertheless, predicting the future is still not an easy task.Therefore, it is more essential to develop diverse future scenarios for establishing policies with a clear vision on the personal, or even national levels rather than attempting to precisely predict a specific future event.Particularly, future research plays a crucial role in the field of disaster management to prevent national crises.Future disasters could also result in an unimaginable scale of damages due to the complex network development of our society.Thus, it is necessary to develop scenarios in advance from the perspective of potential damages caused by disasters.Future scenario development largely comprises quantitative and qualitative methods, which are applied identically in the field of disaster management.Quantitative method is developed using various statistical methods based on numerical data, while qualitative method is developed based on the intellect of a group of experts.In this study, the latter method is used because of the unpredictable nature of disasters.Furthermore, in order to provide a solution for the biased opinions that may occur from the group of experts, big data is used to propose a method for developing future disaster scenarios.The results from this method are preferred to efficiently develop future disaster scenarios, because the opinions of the group of experts are mostly biased.
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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 | 0.000 | 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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".