Training decision-makers in hazard spatial prediction and risk assessment: ideas, tools, strategies and challenges
Bibliographic record
Abstract
Hazard prediction and risk assessment over regions exposed to natural and technological processes are complex tasks that require exposure to quantization of its uncertainty related to the prediction of future events through statistical methods, spatial data analysis, case studies and process evolution interpretation in conditions of uncertainty.All too often decision makers, DMs, similarly to judges in environmental legal practice, do not have technical training to enable them to communicate/understand the associated uncertainty from technical specialists.In particular communication is a challenge with those who can provide prediction maps and associated statistics to support decisions on disaster prevention, avoidance or mitigation.An interactive short course was prepared to overcome such obstacles to responsible land use planning and proactive measure taking, for example, by asking a set of questions.A first phase in the training follows steps that are to facilitate the comprehension of a spatial database on landslide hazard, of its data processing, and of the interpretation of the analysis results.Integral parts of a second phase are the theory of predictive methods, the strategy in prediction map generation and visualization, including validation via blind tests and the representation of the associated spatial and prediction uncertainties.A successive third phase of the training brings in environmental and socioeconomic spatial indicators to assign vulnerabilities and values to exposed elements in the spatial database.Scenarios for hazard development in the future are then provided.They allow to estimate the uncertainty associated with the probabilities of hazardous occurrences and to resolve the risk equation for different settings.The DM training course includes interactive and iterative Disaster Management and Human Health Risk 285
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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 | 0.027 | 0.025 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.009 | 0.008 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.006 | 0.007 |
| Insufficient payload (model declined to judge) | 0.009 | 0.003 |
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".