Optical music recognition and manuscript chant sources
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 increasing variety of digital tools available for medieval musicology research includes the new project Single Interface for Music Score Searching and Analysis (SIMSSA) at McGill University. Currently under development, SIMSSA has begun scanning medieval chant manuscripts and applying optical music recognition (OMR) software to search for musical content. Once thought to be nearly impossible owing to the complexity and stylistic variety of handwritten chant notation (neumes), SIMSSA’s initial ventures have demonstrated that despite the hundreds of different types of medieval signs and the unique characteristics of scribes across medieval Europe, the musical, textual and liturgical content on manuscript pages can be isolated and identified. Manual entry of chant texts and melodies, which is routinely followed by a thorough review, will be supplanted by automated entry, ready for human proofreading. Hours of research time spent collecting data will be saved, and musicologists will be able to move towards analysis of the information much more quickly. With a potentially very large amount of digitized chant data extracted with reduced time and effort, the scope of computer applications for analysis and comparison is considerable. Thriving on the wealth of online digital image libraries, where high-quality photographs of thousands of pages of medieval books are freely available, SIMSSA will not only complement the digital tools currently available to medieval chant researchers, but will bring their varied interests together in a unified online research environment.
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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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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