Why this work is in the frame
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Bibliographic record
Abstract
We consider the integration requirements of modern data intensive applications including data warehousing, global information systems and electronic commerce. At the heart of these requirements lies the schema mapping problem in which a source (legacy) database must be mapped into a different, but xed, target schema. The goal of schema mapping is the discovery of a query or set of queries to map source databases into the new structure. We demonstrate Clio, a new semi-automated tool for creating schema mappings. Clio employs a mapping-by-example paradigm that relies on the use of value correspondences describing how a value of a target attribute can be created from a set of values of source attributes. A typical session with Clio starts with the user loading a source and a target schema into the system. These schemas are read from either an underlying Object-Relational database or from an XML le with an associated XML Schema. Users can then draw value correspondences mapping source attributes into target attributes. Clio's mapping engine incrementally produces the SQL queries that realize the mappings implied by the correspondences. Clio provides schema and data browsers and other feedback to allow users to understand the mapping produced. Entering and manipulating value correspondences can be done in two modes. In the Schema View mode, users see a representation of the source and target schema and create value correspondences by selecting schema objects from the source and mapping them to a target attribute. The alternative Data View mode o ers a WYSIWYG interface for the mapping process that displays example data for both the source and target tables [3]. Users may add and delete value correspondences from this view and immediately see the changes re ected in the resulting target tuples. Also, the Data View mode helps users navigate through alternative mappings, understanding the often subtle di erences between them. For example, in some cases, changing a join from an inner join to an outer join may dramatically change the resulting table. In other cases, the same change may have no e ect due to constraints that hold on the source
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 | 0.000 |
| Open science | 0.000 | 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 it