Bibliographic record
Abstract
Checking the correspondence between two or more database instances and enforcing it is a procedure widely used in practice without however having been explored from a theoretical perspective. In this paper we formally introduce the data correspondence setting and its associated computational problems: checking the existence of solutions, and verifying whether a candidate is a solution, for three distinct types of solutions. Data correspondence is a generalization of data exchange and peer data exchange, and a special case of repairing inconsistent databases. We introduce a new class of dependencies, called semi-LAV, that properly includes both LAV and full dependencies, while retaining tractability for peer data exchange, data correspondence, and database repairs. We also introduce the concept of Σ-satisfying homomorphisms. This new type of homomorphism, together with recent advances, is essential in achieving tractability, while at the same time allowing a large class of dependencies, namely the aforementioned semi-LAV ones. We also show the intractability for a series of problems in the case of weakly acyclic tuple generating dependencies. This implies that many tractability results for weakly acyclic dependencies do not carry over to data correspondence; in these new settings we need to restrict the dependencies a bit further, yielding our semi-LAV dependencies.
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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 | 0.010 | 0.043 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.004 | 0.014 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".