An efficient dynamic optimization method for sequential identification of group-testable items
Why this work is in the frame
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Bibliographic record
Abstract
Group testing with variable group sizes for incomplete identification has been proposed in the literature but remains an open problem because the available solution approaches cannot handle even relatively small problems. This article proposes a general two-stage model that uses stochastic dynamic programming at stage 2 for the optimal group sizes and non-linear programming at stage 1 for the optimal number of group-testable units. By identifying tight bounds on the optimal group size for each step at stage 2 and the optimal initial purchase quantity of the group-testable units at stage 1, an efficient solution approach is developed that dramatically reduces both the number of functional evaluations and the intermediate results/data that need to be stored and retrieved. With this approach, large-scale practical problems can be solved exactly within very reasonable computation time. This makes the practical implementation of the dynamic group-testing scheme possible in manufacturing and health care settings.
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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