DECISION MAKING UNDER UNCERTAINTY USING A VEHICLE MULTILEVEL MODEL: APPROACH TO TARGETS ALLOCATION
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
In this paper, we consider setting performance targets for a vehicle design. The vehicle is modeled by a multilevel hierarchical tree structure. We have considered that each leaf of the structure may have several characteristics, and that for each characteristic a target is defined. Experts’ opinions are expressed with uncertainty regarding the feasibility of achieving these targets. Experts’ opinions are given in the form of intervals associated with their subjective beliefs for the possible values of characteristics. The collected information is propagated in the model to determine the plausibility and the belief for characteristics at the vehicle level. Using this information, five target allocation approaches are discussed which can be applied to three vehicle design strategies.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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 it