Engineering supply chain quality control under different information conditions
Why this work is in the frame
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Bibliographic record
Abstract
Within a three-level engineering supply chain that includes the owner, general contractor, and subcontractor, the optimal quality control strategy of the owner under symmetric, asymmetric, and incomplete information was studied. Using the quality control level of the general contractor and subcontractors, as well as the quality supervision level of the general contractor, and the quality supervision level of the owner as decision variables, and the cost function of each party as a quadratic function, the optimal quality control strategy of the owner under symmetric and asymmetric information is derived based on the maximum value method and Lagrange multiplier method. Under incomplete information, the optimal quality control strategy of the owner is derived when the probability density function of the general contractor's quality control level and quality supervision level follows a triangular distribution. Through simulation calculations, the results under different information conditions were analyzed.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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