Bibliographic record
Abstract
With the tremendous growth in data science and machine learning, it has become increasingly clear that traditional relational database management systems (RDBMS) are lacking appropriate support for the programming paradigms required by such applications, whose developers prefer tools that perform the computation outside the database system. While the database community has attempted to integrate some of these tools in the RDBMS, this has not swayed the trend as existing solutions are often not convenient for the incremental, iterative development approach used in these fields. In this paper, we propose AIDA - an abstraction for advanced in-database analytics. AIDA emulates the syntax and semantics of popular data science packages but transparently executes the required transformations and computations inside the RDBMS. In particular, AIDA works with a regular Python interpreter as a client to connect to the database. Furthermore, it supports the seamless use of both relational and linear algebra operations using a unified abstraction. AIDA relies on the RDBMS engine to efficiently execute relational operations and on an embedded Python interpreter and NumPy to perform linear algebra operations. Data reformatting is done transparently and avoids data copy whenever possible. AIDA does not require changes to statistical packages or the RDBMS facilitating portability.
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 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 | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.004 | 0.009 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.310 | 0.279 |
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".