Modelling weather risk preferences with multi‐criteria decision analysis for an aerospace vehicle launch
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
ABSTRACT Decision‐making under weather uncertainty is a challenge in several fields. When the decision process involves many stakeholders, frequently with different interpretations of the meteorological information, the process is even more complex. This work provides a quantitative decision model with a new index (called the weather decision index, WDI) to support the stakeholders in making real‐world choices according to their preferences regarding the uncertainty of weather information. The integrated model combines several methods such as problem structuring, multi‐criteria analysis, scenario planning and probabilistic weather forecast techniques. As a demonstration, the model was applied in the sounding rocket launch mission in the Brazilian Space Programme. The WDI captured stakeholders' behaviour related to three meteorological information attributes (probability, lead‐time and variables) and modelled the most important judgements of the decision maker; low probability or an extended lead‐time depreciates the meteorological information, and weather variables are not considered in the decisions, even with forecasts of extreme events. Modelling with the WDI brings a new perspective in weather‐related decision problems. The choice of alternatives no longer depends on a necessarily simplified optimization analysis, but rather on the decision maker's preferences about the possibly nonlinear trade‐offs between forecast reliability and lead‐time. The findings also increase understanding of the forecast decision maker's preferences and how to improve weather risk communication. The WDI provides a starting point for several applications, including early warning systems or climate change adaptation, for which reliable uncertainty estimates are accessible.
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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 | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it