Bibliographic record
Abstract
With the extensive use of XML in applications over the Web, how to update XML data is becoming an important issue because the role of XML has expanded beyond traditional applications, in which XML is used as a mean for data representation and exchange on the Web. This paper presents a novel declarative XML update language, which is an extension of the XML-RL query language. Compared with other existing XML update languages, it has the following features. First, it is the only XML data manipulation language based on a higher data model. All of the other update languages adopt so-called graph-based or tree-based data models. Therefore, update requests can be expressed in a more intuitive and natural way in our language than in the other languages. Second, our language is designed to deal with ordered and unordered data. Some of the existing languages cannot handle the order of documents. Third, our language can express complex update requests at multiple level in a hierarchy in a simple and fast way. Some existing languages have to express such complex requests in nested updates, which is too complicated and nonintuitive to comprehend for end users. Fourth, our language directly supports the functionality of updating complex objects while all other update language do not support these operations. Lastly, most of existing languages use rename to modify attribute and element names, which is a different way from updates on value. Our language modifies tag names, values, and objects in a unified way by the introduction of three kinds of logical binding variables: object variables, value variables, and name variables. The powerful ability of our language is shown by various examples.
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 machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.010 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 0.004 |
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 source (direct Gemma or distilled Codex), 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".