Bibliographic record
Abstract
This dataset contains specific data of 86 classified modal cycles, complete and incomplete. A modal cycle is an ordered set of compositions that go through the modes. The ordering is done by the composer or by an editor. The genre can be compared to the more recent Well-Tempered Clavier by J.S. Bach. There are two files in this dataset: Multif0.zip contains multif0 extractions of 1201 recordings of (parts) of modal cycles. experiment_metadata.csv contains the meadata of the records in the dataset. The multif0 extractions are obtained using the model by Cuesta et al. (2020): Cuesta, H.; McFee, B.; Gómez, E. Multiple f0 estimation in vocal ensembles using convolutional neural networks. In Proceedings of the International Society for Music Information Retrieval (ISMIR), Montréal, Canada, 2020. The recordings are collected in a Spotify playlist: https://open.spotify.com/playlist/269faEhQG4UvDJ3KJ9tWhd?si=f236f2c3aa9340ff The source of the modal cycles in this playlist is the book Wiering, Frans (2001). The Language of the Modes: Studies in the History of Polyphonic Modality. Routledge.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.003 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.031 | 0.060 |
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".