Salzinnes and Digital Tools
Bibliographic record
Abstract
Abstract This paper discusses the digital resources that have emerged from work with the Salzinnes antiphoner and incorporates several different perspectives. Jennifer Bain provides a history of the collaboration with Ichiro Fujinaga’s Distributed Digital Music Archives and Libraries (DDMAL) Lab and how the Salzinnes Antiphonal became so integral for research there. Elsa De Luca, who has been a key member in the development of the neume module for the Music Encoding Initiative (MEI), addresses challenges associated with adapting the MEI principles to earlier, complex chant notations. Geneviève Gates-Panneton and Dylan Hillerbrand discuss how the Salzinnes Antiphonal was used as a base manuscript for tools recently developed at the McGill DDMAL Lab for Optical Music Recognition of chant manuscripts and for the facilitation of the process of editing computer-generated data. These musicologist-friendly tools are designed for the online correction and emendation of computer-generated data created automatically from digital scans of manuscripts.
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.
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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.003 | 0.020 |
| Scholarly communication | 0.014 | 0.009 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.022 | 0.004 |
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".