Bi-Level Mapping: Combining Schema and Data Level Heterogeneity in Peer Data Sharing Systems.
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
Abstract. Peer data sharing systems use either schema-level or data-level mappings to resolve schema as well as data heterogeneity among data sources (peers). Schema-level mappings create structural relationships among different schemas. On the other hand, data-level mappings associate data values in two different sources. These two kinds of mappings are complementary to each other. However, existing peer database systems have been based solely on either one of these mappings. We believe that if both mappings are addressed simultaneously in a single framework, the resulting approach will enhance data sharing in a way such that we can overcome the limitations of the non-combined approaches. In this paper, we introduce a model of a peer database management system(PDMS) which uses a mapping that combines schema-level and data-level mappings. We call this new kind of mapping bi-level mapping. We present the syntax and semantics of bi-level mappings. We also provide a query evaluation procedure for the PDMS that uses the bi-level mappings.
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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 | 0.002 | 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 | 0.004 |
| Open science | 0.003 | 0.006 |
| 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