SCODED: Statistical Constraint Oriented Data Error Detection
Bibliographic record
Abstract
Statistical Constraints (SCs) play an important role in statistical modeling and analysis. This paper brings the concept to data cleaning and studies how to leverage SCs for error detection. SCs provide a novel approach that has various application scenarios and works harmoniously with downstream statistical modeling. Entailment relationships between SCs and integrity constraints provide analytical insight into SCs. We develop SCODED, an SC-Oriented Data Error Detection system, comprising two key components: (1) SC Violation Detection : checks whether an SC is violated on a given dataset, and (2) Error Drill Down : identifies the top-k records that contribute most to the violation of an SC. Experiments on synthetic and real-world data show that SCs are effective in detecting data errors that violate them, compared to state-of-the-art approaches.
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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 | 0.012 | 0.070 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.005 | 0.006 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".