Bibliographic record
Abstract
We present CONDOR, a tool for managing constraints towards improved data quality. As increasing amounts of heterogeneous data are being generated, integrity constraints are the primary tool for enforcing data integrity. It is essential that an accurate and up-to-date set of constraints exist to validate that the correct application semantics are being enforced. We consider the widely used constraint, functional dependencies (FDs). CONDOR is an integrated system that identifies inconsistent data values (along with suggestions for clean values), and generates repairs to both the data and/or FDs to resolve inconsistencies. We extend the set of FD repair operations proposed in past work, by (1) adding a set of attributes to an FD; (2) transforming an FD to a conditional functional dependency (CFD); and (3) identifying redundant attributes in an FD. Our demonstration will showcase the visualization and interactive features of CONDOR to help users determine the best repairs that resolve the underlying inconsistencies to improve data quality.
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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 | 0.006 | 0.023 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.005 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.087 | 0.024 |
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".