Perceptual interactions of pitch and timbre: an experimental study on pitch-interval recognition with analytical applications
Bibliographic record
Abstract
List of ExamplesExample 1.1 Esprit Rude/Esprit Doux, mm 1-4 66 Example 1.2 Esprit Rude/Esprit Doux, mm 1-4, combined version 67 Example 1.3 Esprit Rude/Esprit Doux, mm 1-4, phrase divisions 68 Example 2.1 Carter Esprit Rude/Esprit Doux, mm 83-88 71 Example 2.2 Esprit Rude/Esprit Doux, mm 83-88, combined version with phrase boundaries 73 Example 3 Esprit Rude/Esprit Doux, mm 32-35 74 Example 4.1 Rigmarole, mm 15-25 76 Example 4.2 Rigmarole, mm 15-25, piano (timbre neutral) 79 Example 5.1 Au Quai, mm 12-17, perceived monophonic line 80 Example 5.2 Au Quai mm 12-17, piano (timbre neutral) phrase boundaries 82 Example 6.1 Quartet op.22, mvt II, mm 68-88, original 85 Example 6.2 Quartet op.22, mm 75-78, monophonic line 86 Example6.3Quartet op.22, measures 81-84, monophonic line 87 Example 6.4 Quartet op.22, mm 70-72 90 Example 6.5 Quartet op.22, measures 85-88 90 Example 6.6 Quartet op.22, mm 68-88, summary 92 Example 7.1 Concerto op. 24, mvt II, mm 1-28, analysis summary of Bailey, Wintle and Spinner 97 Example 7.2 Concerto op. 24, mvt II, mm 1-28, piano reduction 100 vi Example 7.3 Concerto op. 24, mvt II, mm 1-11, Antecedent phrase in Clarinet version 102 Example 7.4 Concerto op. 24, mvt II, mm 11-22, Consequent phrase in Clarinet version 103 Example 7.5 Concerto op. 24, mvt II, mm 23-28, Extension in Clarinet version 103 Example 7.6 Concerto op. 24, mvt II, mm 1-10, Antecedent phrase constructed as a Sentence 105 Example 7.7 Concerto op. 24, mvt II, mm 11-22, Consequent phrase constructed as Sentence 108 Example 7.8 Concerto op. 24, mvt II, mm 23-28, Extension 109 vii
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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 | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".