Machine Learning and Data Cleaning: Which Serves the Other?
Bibliographic record
Abstract
The last few years witnessed significant advances in building automated or semi-automated data quality, data cleaning and data integration systems powered by machine learning (ML). In parallel, large deployment of ML systems in business, science, environment and various other areas started to realize the strong dependency on the quality of the input data to these ML models to get reliable predictions or insights. That dual relationship between ML and data cleaning has been addressed by many recent research works under terms such as “Data cleaning for ML” and “ML for automating data cleaning and data preparation”. In this article, we highlight this symbiotic relationship between ML and data cleaning and discuss few challenges that require collaborative efforts of multiple research communities.
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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 | 0.072 | 0.112 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.002 |
| Bibliometrics | 0.007 | 0.010 |
| Science and technology studies | 0.005 | 0.023 |
| Scholarly communication | 0.017 | 0.044 |
| Open science | 0.005 | 0.012 |
| Research integrity | 0.013 | 0.017 |
| Insufficient payload (model declined to judge) | 0.008 | 0.005 |
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".