Simplified access to structured databases by adapting keyword search and database selection
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
This paper presents a tool that enables non-technical (naive) end-users to use free-form queries in exploring distributed relational databases with simple and direct technique, in a fashion similar to using search engines to search text files on the web. This allows web designers and database developers to publish their databases for web browsers exploring. The proposed approach can be used for both Internet and Intranet application areas. Our approach depends on identifying first databases that are most likely to provide useful results to the raised query, and then search only the identified databases. In our work, we developed and extended an estimation technique to assess the usefulness measure of each database. Our technique has been borrowed from the similar techniques used for information retrieval (IR), mainly for text and document databases; it supports working smoothly with the structured information stored in relational databases. Such a usefulness measure enables nave users to make decisions about databases to search and in what order.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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