Bibliographic record
Abstract
Introduction Information integration and interoperability among information sources are related problems that have received significant attention since early days of computer information processing. Initially, for a few decades, the focus was on integration/interoperability for a relatively small number of sources. This is the setting encountered in traditional business and service applications, for example when two companies merge or several services interoperate (which requires the integration of their information systems). Much of the work in this context of federated or multi-databases focused on integrating schemas by defining a global schema in an expressive data model and defining mappings from local schemas to the global one [19]. More recently, in the context of integration of data sources on the internet, the so-called global-as-view (GAV) and local-as-view (LAV) paradigms have emerged out of projects such as TSIMMIS [20] and Information Manifold (IM) [12]. Recently, the ad
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.014 |
| Science and technology studies | 0.002 | 0.009 |
| Scholarly communication | 0.010 | 0.018 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.006 | 0.005 |
| Insufficient payload (model declined to judge) | 0.017 | 0.005 |
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".