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 with guaranteed reliability is hard to achieve without accessing external sources, since the truth is not necessarily discoverable from the data at hand. Furthermore, even in the presence of external sources, mainly knowledge bases and humans, effectively leveraging them still faces many challenges, such as aligning heterogeneous data sources and decomposing a complex task into simpler units that can be consumed by humans. We present K atara , a novel end-to-end data cleaning system powered by knowledge bases and crowdsourcing. Given a table, a kb , and a crowd, K atara (i) interprets the table semantics w.r.t. the given kb ; (ii) identifies correct and wrong data; and (iii) generates top- k possible repairs for the wrong data. Users will have the opportunity to experience the following features of K atara : (1) Easy specification: Users can define a K atara job with a browser-based specification; (2) Pattern validation: Users can help the system to resolve the ambiguity of different table patterns ( i.e. , table semantics) discovered by K atara ; (3) Data annotation: Users can play the role of internal crowd workers, helping K atara annotate data. Moreover, K atara will visualize the annotated data as correct data validated by the kb , correct data jointly validated by the kb and the crowd, or erroneous tuples along with their possible repairs.
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 | 0.004 | 0.002 |
| 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 | 0.000 |
| Open science | 0.002 | 0.001 |
| 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