A Finite Multi-Dimensional Generalized Gamma Mixture Model
Why this work is in the frame
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Bibliographic record
Abstract
Over the last two decades, statistical mixture models have been widely exploited to tackle the issue of data modeling. Examples of statistical mixture models' applications in data modeling include object recognition, speech recognition, information retrieval, and intrusion detection. In this paper, an unsupervised learning algorithm, based on a finite multi-dimensional generalized Gamma mixture model (GGMM) is presented for the purpose of positive vectors clustering. Maximum likelihood (ML) is a well-known method conducted via expectation maximization algorithm (EM) and used for estimating the parameters of the proposed model. Newton Raphson's optimization algorithm was also utilized to solve the problem (obstacle) of the non-existence of closed form. Experiments are conducted using both synthetic data and a real data set of images representing shapes to test the performance of the proposed model. Moreover, we compared the performance of the generalized Gamma mixture model with Gamma and Gaussian mixture models.
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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 | 0.001 | 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