Concise descriptions of subsets of structured sets
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
We study the problem of economical representation of subsets of structured sets, which are sets equipped with a set cover or a family of preorders. Given a structured set U , and a language L whose expressions define subsets of U , the problem of minimum description length in L (L-MDL) is: “given a subset V of U , find a shortest string in L that defines V .” Depending on the structure and the language, the MDL-problem is in general intractable. We study the complexity of the MDL-problem for various structures and show that certain specializations are tractable. The families of focus are hierarchy, linear order, and their multidimensional extensions; these are found in the context of statistical and OLAP databases. In the case of general OLAP databases, data organization is a mixture of multidimensionality, hierarchy, and ordering, which can also be viewed naturally as a cover-structured ordered set. Efficient algorithms are provided for the MDL-problem for hierarchical and linearly ordered structures, and we prove that the multidimensional extensions are NP-complete. Finally, we illustrate the application of the theory to summarization of large result sets and (multi) query optimization for ROLAP queries.
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 | 0.001 | 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