Bibliographic record
Abstract
XML employs a tree-structured model for representing data. Queries in XML query languages, for example, XPath (World Wide Web Consortium, 1999), XQuery (World Wide Web Consortium, 2001), XML-QL (Deutch, Fernandex, Florescu, Levy, & Suciu, 1999), and Quilt (Chamberlin, Clark, Florescu, & Stefanescu 1999; Chamberlin, Robie, & Florescu, 2000), typically specify patterns of selection predicates on multiple elements that have some specified tree structured relationships. For instance, the following XPath expression: a[b[c and //d]]/b[c and e//d] asks for any node of type b that is a child of some node of type a. In addition, the b-node is the parent of some c-node and some e-node, as well as an ancestor of some d-node. In general, such an expression can be represented by a tree structure as shown in Figure 1(a). In such a tree pattern, the nodes are types from S ? {*} (* is a wildcard, matching any node type), and edges are parent-child or ancestor-descendant relationships. Among all the nodes of a query Q, one is designated as the output node, denoted by output(Q), corresponding to the output of the query.
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.
How this classification was reachedexpand
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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".