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
Many Web data sources and APIs make their data available in XML, JSON, or a domain-specific semi-structured format, with the goal of making the data easily accessible and usable by Web application developers. Although such data formats are more machine-processable than pure text documents, managing and analyzing such data in large scale is often nontrivial. This is mainly due to the lack of a well-defined (or understood) structure and clear semantics in such data formats, which could result in poor data quality. In the xCurator project, we add structure to such data with the goal of publishing it on the Web as Linked Data. We enhance the quality of such data by: extracting entities, their types, and their relationships to other entities; performing entity (and entity type) identification; merging duplicate entities (and entity types); linking related entities (internally and to external sources); and publishing the results on the Web as high-quality Linked Data. This is all in a light-weight easy-to-use and scalable framework that eectively incorporates user feedback in all phases. We describe the initial framework of our system and report the results of using our system for managing large volumes of (user-generated) data on the Web in several real world applications.
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 | 0.001 |
| 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 | 0.000 |
| Open science | 0.003 | 0.001 |
| 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