Semantic grep: regular expressions + relational abstraction
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
Searching source code is one of the most common activities of software engineers. Text editors and other support tools normally provide searching based on lexical expressions (regular expressions). Some more advanced editors provide a way to add semantic direction to some of the searches. Recent research has focused on advancing the semantic options available to text-based queries. Most of these results make use of heavy weight relational database management technology. In this paper we explore the extension of lexical pattern matching by means of light weight relational queries, implemented using a tool called grok. A "semantic grep" (sgrep) command was implemented, which translates queries in a mixed algebraic and lexical language into a combination of grok queries and grep commands. This paper presents the design decisions behind sgrep, and example queries that can be posed. The paper concludes with a case study in which sgrep was used to identify architectural anomalies in PostgreSQL, an open source Database Management System.
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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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