Infinite Shoreline for Orchestra
Bibliographic record
Abstract
Infinite Shoreline, by Colin Spencer McMahon, DMA 2021Graduate Department of Music, University of Toronto Infinite Shoreline is a composition for orchestra in one movement that explores the mathematical, philosophical, and emotional implications of the “Coastline Paradox”. The “Coastline Paradox” highlights the difference between our experience of the natural world, and reality as observed by fractal geometry. A fractal is a curved line that can have no discernible length, its complexity changes with scale. If you were to measure the distance between plotted points on a coastline and then sum the total, the total would increase as you increased the number of plotted points such that as the number of points approaches infinite so does the length of the coastline. Infinite Shoreline explores ornamentation as a method for melodic development, wherein a broad, simple melody is made increasingly complex whist still following the same essential landmarks. Ornamentation is used as an allegory for the infinite, fractal nature of a coastline. As the melody increases in complexity so too does the linear harmonic collections that contain it, moving from pentatonic, through to 12 tones and beyond to micro-tonal as the melodic complexity “approaches infinite” so-to-speak. If the coastline is a melody, the harmonic and textural contexts in which we hear it is the ocean; constantly shifting, on minuscule, local, and large scales. At the point of contact the ocean is in constant flux (waves), it shifts dramatically with the moon, and the ocean is slowly rising (global warming.) This constant motion influences the make-up of the coastline as defined as “the place where the ocean and land meet”, but also on the land itself through eroding and depositing earth. In musical terms, the turbulent rising and falling of orchestral texture and harmonic density shifts the otherwise firm landmarks of the melody. Approximate duration: 14’ 00”
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.157 | 0.067 |
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".