Rules for Identifying the Initial Design Points for Use in the Quick Convergent Inflow Algorithm
Bibliographic record
Abstract
The starting point of search is an important factor in optimal design construction as a poor starting point may require a longer time before convergence is reached. Hence the location of the initial design points for use in the Quick Convergent Inflow Algorithm on segmented regions is examined with the aim of developing useful criteria for identifying the initial design points. Proportional allocation of design points to go into the initial design measures is proposed. The allocation of 100% vertex points, 100% boundary points and 100% boundary points as well as the allocation of 50% vertex and 50% boundary points, 50% vertex and 50% interior points and 50% interior and 50% boundary points are investigated. Results show that a combination of design points comprising of 50% vertex points and 50% interior points or 50% vertex points and 50% boundary points forms helpful rules in identifying the initial design points for use in the Quick Convergent Inflow Algorithm. With these combinations, a moderate number of iterations needed to reach the required optimal or near-optimal solution is maintained.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".