A morphology of medieval notations in the Optical neume recognition project
Bibliographic record
Abstract
The study of medieval notations depends on effective categorization of individual signs in order to facilitate a comprehensive understanding of their musical meaning. Over the past century, chant scholars have developed several kinds of neume tables which arrange and contextualize neumes either according to graphical type, chronology, or scribal tradition. Some neume tables contain longer strings of neumes that link certain notation conventions with performance traditions. The course of neume table development reads like a history of the study of early notations, itself, and reveals the evolving interests and pursuits of the scholars who created them. It also sets the stage for the latest use of the neume table as a reference for document analysis software applied to digital images of medieval manuscripts. Now, instead of presenting a static list of discrete signs, the neume table can be understood as a reflection of the notational variety and nuance of the hundreds of thousands of neumes contained in every book of liturgical chant. On this scale, neume tables help scholars to understand the use of medieval neumes in the same way a linguist understands the morphology of words. This article presents the principles on which this new kind of neume table has been developed and suggests the ways in which this new way of thinking might inform the discipline in the future.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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 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".