Runner balancing by a direct genetic optimization of shrinkage
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 The proposed approach to the runner‐balancing problem evaluates differences in shrinkage among the cavities and uses this direct measure of product quality to balance runner systems instead of the indirect methods traditionally used. The runner‐balancing problem was characterized by multiple objectives, which consider both cost and product quality. The resulting multi‐objective optimization problem was solved with a multi‐objective genetic algorithm. Runner‐balancing optimizations varied the diameters and lengths of the runners and the processing conditions. The results suggest that balanced runner systems, which exhibit large differences in cavity pressure profiles, can have lower product costs than systems characterized by similar fill times and cavity pressure profiles. The optimization of the secondary runner lengths and processing conditions also reduced costs significantly. Polym. Eng. Sci. 44:1949–1959, 2004. © 2004 Society of Plastics Engineers.
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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 | 0.000 |
| 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