Bibliographic record
Abstract
Organizations use data to support different business processes. Data may become unclean because of corruptions in the central quality aspects due to factors such as duplicate records, outdated data, inconsistent values, incomplete information, or inaccurate values. Real datasets are usually not available for reasons such as privacy constraints. In the existing systems that generate or corrupt synthetic data, the intrinsic characteristics of data may not satisfy the quality aspects, and the injected types of errors do not corrupt multiple data quality aspects. Also, a lack of common datasets is a primary reason that representative comparisons between algorithms of different data quality management approaches are not possible. To address these issues, we present datumPIPE, a system that allows for the generation of data that satisfies a set of integrity constraints, including functional dependencies (FDs), conditional functional dependencies (CFDs), and inclusion dependencies (INDs). Also, datumPIPE provides the functionality to generate other types of attribute values such as sensors and personal data. It also allows for the corruption of the generated data through the introduction of quality issues in the central data quality aspects.
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.
How this classification was reachedexpand
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 | 0.003 |
| 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 | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.007 |
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; both teacher heads agree on what is shown here.
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".