Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Summarization of query results is an important problem for many OLAP applications. The Minimum Description Length principle has been applied in various studies to provide summaries. In this paper, we consider a new approach of applying the MDL principle. We study the problem of finding summaries of the form S ⊖ H for k-d cubes with tree hierarchies. The S part generalizes the query results, while the H part describes all the exceptions to the generalizations. The optimization problem is to minimize the combined cardinalities of S and H. We first characterize the problem by showing that solving the 1-d problem can be done in time linear to the size of hierarchy, but solving the 2-d problem is NPhard. We then develop three different heuristics, based on a greedy approach, a dynamic programming approach and a quadratic programming approach. We conduct a comprehensive experimental evaluation. Both the dynamic programming algorithm and the greedy algorithm can be used for different circumstances. Both produce summaries that are significantly shorter than those generated by state-of-the-art alternatives. 1
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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