Bibliographic record
Abstract
The Cantus database is a well-established project devoted to the creation and distribution of electronic indices of manuscript and early printed sources of Latin chant for the liturgical Office. As of January 2011, there were over 379,000 records in the database, each of which is an individual chant in one of the 134 manuscripts which have been indexed to date. For over a decade, this research tool has been growing and adapting to the needs of chant scholars, musicologists, hagiographers, art historians and researchers in other fields. In addition to the basic search functions and downloading options, there are now several analytical tools available on the website, including a textual concordance and an interactive dendrogram-creation tool. The latter, an example of data-mining, allows the user to select a series of chants which will form the basis of a comparison among the numerous manuscripts whose contents are recorded in Cantus. Similarities in chant series can be interpreted as affinities among manuscripts, and so, the dendrograms which are created (through the calculations of similarity matrices) can assist researchers in identifying related chant repertories, in studying the origins and dissemination of saints' feasts, in providing evidence for the provenance of manuscript sources and, undoubtedly, for numerous other research applications.
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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.018 | 0.019 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 0.005 |
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".