Bibliographic record
Abstract
Mathematical Information Retrieval (MIR) has been actively studied in recent years and many fruitful results have emerged. Among those, the Approach Zero system is one of the few math-aware search engines that is able to perform substructure matching efficiently. Furthermore, it has been deployed in ARQMath2020, the most recent community-wide MIR evaluation, as a strong baseline due to its empirical effectiveness and ability to handle structured math content. However, in order to implement a retrieval model that handles structured queries efficiently, Approach Zero is written in C from the ground up, requiring special pipelines for processing math content and queries. Thus, the system is not conveniently accessible and reusable to the community as a research tool. In this paper, we present PyA0, an easy-to-use Python toolkit built on Approach Zero that improves its accessibility to researchers. We introduce the toolkit interface and report evaluation results on popular MIR datasets to demonstrate the effectiveness and efficiency of our toolkit. We have made PyA0 source code publicly accessible at https://github.com/approach0/pya0, which includes a link to a notebook demo.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.006 |
| Open science | 0.005 | 0.005 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.047 | 0.050 |
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".