DML 2009 Towards a Digital Mathematics Library Grand Bend,Ontario, Canada July 8-9th, 2009 Proceedings Preface
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
The 12 contributions are divided into five parts. Topics include: * search, indexing and retrieval of mathematical documents; * ranking of mathematical papers, similarity of mathematical documents; * math OCR with MathML/TeX output; * document conversions from/to MathML, OpenMath, LaTeX, PostScript and [tagged] PDF; * mathematical document compression; * processing of scanned images; * algorithms for crosslinking of bibliographical items, intext citations search; * mathematical document classification, MSC 2010; * mathematical text mining; * mathematical documents metadata exchange via OAI-PMH and/or OAI-ORE; * long term archiving, data migration; * reports and experience from math digitization projects; * math publishing with long term archival goal; * software engineering aspects of creating, handling MathML, OMDoc, OpenMath documents, and displaying them in web browsers.
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 | 0.002 | 0.006 |
| Open science | 0.001 | 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