Bow River Falls (audio recording)
Bibliographic record
Abstract
Bow River Falls is a collaborative ensemble recorded during a teaching residency at the Banff International Workshop in Jazz and Creative Music in Banff, Alberta. The ensemble includes trumpeter Dave Douglas, clarinetist Louis Sclavis, cellist Peggy Lee, and drummer/laptop musician Dylan van der Schyff. Reflecting the natural beauty of Banff, the musicians create atmospheric and organic pieces that reference '60s free jazz and contemporary classical chamber music – often recalling the iconic Ornette Coleman Quartets and the Art Ensemble of Chicago. The musicians utilize their instruments in unconventional ways, employing extended techniques to summoning growls, bleeps, and pinched squelches. Additionally, the laptop inclusions by van der Schyff are done live in the studio and have an organic quality, blending gurgling white noise, static, and other visceral found sounds with his acoustic percussion. Dave Douglas (trumpet) Louis Sclavis (clarinet, bass clarinet) Peggy Lee (cello) Dylan van der Schyff (drums, percussion, laptop) Recorded in June, 2003, at the Banff Centre John Sorenson - Audio Engineer, Engineer, Mixing Pablo Mochcovsky - Mastering Produced by Dave Douglas and Dylan van der Schyff Originally Released on Premonition Records (2004)
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.501 | 0.175 |
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".