P-06 Musical Composition Fostering Project
Bibliographic record
Abstract
This past summer I travelled to a rural part of British Columbia, Canada for the express purpose of creating music within its highly- conducive environment, an aspect of my AU FRG-sponsored activity. I composed mostly outside, sitting in a pasture with an ebb- and-flow of some 35 alpacas, three horses and occasional dogs, often stopping to soak in the inspiring environment, to photograph in nature, and to exercise (including bicycle riding in the pasture). On average, I composed for approximately 40 hours per week, for about one month. This process was extraordinarily productive (even while mostly done less efficiently with pencil and paper instead of with computer and keyboard), yielding drafts of approximately a dozen new musical works. The major aspect was creating compressed-score drafts (“short scores”) for most of a four-movement concert band suite concerning Adventist history in Battle Creek, Michigan—publishing, college, tabernacle and cemetery. I am planning to complete this by 2015, the centennial of Ellen G. White’s death. Other drafts involved vocal, congregational or choral music including texts by George Herbert, Christina Rossetti and Christopher Smart.
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 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 | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".