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
Keys are very important in many aspects of data management, such as guiding query formulation, query optimization, indexing, etc. We consider the situation where an XML document does not come with key definitions, and we are interested in using data mining techniques to obtain a representation of the keys holding in a document. In order to have a compact representation of the set of keys holding in a document, we define a partial order on the set of all key expressions. This order is based on an analysis of the properties of absolute and relative keys for XML. Given the existence of the partial order, only a reduced set of key expressions need to be discovered.Due to the semistructured nature of XML documents, it turns out to be useful to consider keys that hold in "almost" the whole document, that is, they are violated only in a small part of the document. To this end, the support and confidence of a key expression are also defined, and the concept of approximate key expression is introduced. We give an efficient algorithm to mine a reduced set of approximate keys from an XML document.
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 | 0.002 |
| Open science | 0.002 | 0.002 |
| 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