Three related types of multi-value association patterns
Why this work is in the frame
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Bibliographic record
Abstract
Mining patterns involving multiple values that are significantly relevant is a difficult but very important problem that crosses many disciplines. Multi-value association patterns, which generalize sequential pattern, are sets of associated values extracted from sampling outcomes of a random N-tuple. Because they are value patterns from multiple variables, they are more descriptive than their corresponding variable patterns. Hence, they are also easier to interpret. Normally, they can be detected by statistical testing if the occurrence of a pattern event is significantly deviated from the expected according to a prior model or null hypothesis. In this paper, we consider three related types of multi-value association patterns including high-order pattern (HOP), consigned pattern (CP), and nested high-order pattern (NHOP). We further evaluate the nested high-order pattern and its relationships to the others using experiments.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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