Bibliographic record
Abstract
An association pattern describes how a group of items (for example, retail products) are statistically associated together, and a meaningful association pattern identifies ‘interesting’ knowledge from data. A wellestablished association pattern is the association rule (Agrawal, Imielinski & Swami, 1993), which describes how two sets of items are associated with each other. For example, an association rule A-->B tells that ‘if customers buy the set of product A, they would also buy the set of product B with probability greater than or equal to c’. Association rules have been widely accepted for their simplicity and comprehensibility in problem statement, and subsequent modifications have also been made in order to produce more interesting knowledge, see (Brin, Motani, Ullman and Tsur, 1997; Aggarwal and Yu, 1998; Liu, Hsu and Ma, 1999; Bruzzese and Davino, 2001; Barber and Hamilton, 2003; Scheffer, 2005; Li, 2006). A relevant concept is the rule interest and excellent discussion can be found in (Shapiro 1991; Tan, Kumar and Srivastava, 2004). Huang et al. recently developed association bundles as a new pattern for association analysis (Huang, Krneta, Lin and Wu, 2006). Rather than replacing the association rule, the association bundle provides a distinctive pattern that can present meaningful knowledge not explored by association rules or any of its modifications.
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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 | 0.005 | 0.026 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.011 | 0.011 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.006 | 0.008 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.044 | 0.035 |
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".