Generalization for calendar attributes using domain generalization graphs
Why this work is in the frame
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Bibliographic record
Abstract
The paper addresses the problem of generalizing temporal data based on calendar (date and time) attributes The proposed method is based on a domain generalization graph, i.e., a lattice defining a partial order that represents a set of generalization relations for the attribute. The authors specify the components of a domain generalization graph suited to calendar attributes. They define granularity, subset, lookup, and algorithmic methods for specifying generalizations between calendar domains. To reduce the size of the domain generalization graph used in generalization and the number of results shown to the user, they use six types of pruning: reachability pruning, preliminary manual pruning, data range pruning, previous discard pruning, pregeneralization manual pruning, and post generalization pruning.
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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 | 0.001 |
| 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