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
Data cleaning has played a critical role in ensuring data quality for enterprise applications. Naturally, there has been extensive research in this area, and many data cleaning algorithms have been translated into tools to detect and to possibly repair certain classes of errors such as outliers, duplicates, missing values, and violations of integrity constraints. Since different types of errors may coexist in the same data set, we often need to run more than one kind of tool. In this paper, we investigate two pragmatic questions: (1) are these tools robust enough to capture most errors in real-world data sets ? and (2) what is the best strategy to holistically run multiple tools to optimize the detection effort? To answer these two questions, we obtained multiple data cleaning tools that utilize a variety of error detection techniques. We also collected five real-world data sets, for which we could obtain both the raw data and the ground truth on existing errors. In this paper, we report our experimental findings on the errors detected by the tools we tested. First, we show that the coverage of each tool is well below 100%. Second, we show that the order in which multiple tools are run makes a big difference. Hence, we propose a holistic multi-tool strategy that orders the invocations of the available tools to maximize their benefit, while minimizing human effort in verifying results. Third, since this holistic approach still does not lead to acceptable error coverage, we discuss two simple strategies that have the potential to improve the situation, namely domain specific tools and data enrichment. We close this paper by reasoning about the errors that are not detectable by any of the tools we tested.
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.
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 | 0.005 | 0.005 |
| 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 | 0.001 |
| Open science | 0.004 | 0.004 |
| 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