An Efficient Poisson-Distributed Adaptive Cluster Sampling Model Using Randomized Response Strategy
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Bibliographic record
Abstract
The key innovation lies in the incorporation of an adaptive cluster sampling strategy and a randomized response model based on the Poisson distribution.This integration aims to overcome shortcomings inherent in conventional models, providing a more robust framework for research area.In this paper, an adaptive cluster sampling randomized response model with Poisson distribution using a randomized response strategy was proposed.The proposed cluster randomized response model has improved efficiency and a large gain in precision.Conditions were obtained under which the proposed model is more efficient than the existing models.To validate the effectiveness of our approach, numerical computations were conducted, offering concrete illustrations of the model's performance.The results underscore the significant gains in efficiency and precision achieved by the proposed adaptive cluster sampling randomized response model.
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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 | 0.005 | 0.002 |
| 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 | 0.001 | 0.002 |
| 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