5.4.4 Managing Customer Expectations through an Integrated Approach to Risk Management, Program Performance Measures, and Trade Studies
Why this work is in the frame
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Bibliographic record
Abstract
Abstract Standard practices in systems engineering do not take full advantage of data collected and organized by those performing risk management, program performance measurement, and trade study management tasks. Adjustments to existing systems engineering practices, presented in this paper, allow integration of these separate tasks in a way that significantly enhances program management's ability to manage customer expectations and reduce the cost of development. This paper presents real‐world examples of extensions to risk assessment practices that allow risks to be rolled‐up to program performance measures in addition to a traditional work breakdown structure. Integration of existing risk abatement planning tasks are explained in sufficient detail to show how key decisions embedded in risk abatement plans are included on the trade study list and evaluated in terms of program performance measures.
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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 | 0.000 | 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 it