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Record W7035770106

Affective Qualities of Sustained Instrumental Blends

2024· dissertation· en· W7035770106 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueeScholarship@McGill (McGill) · 2024
Typedissertation
Languageen
FieldAgricultural and Biological Sciences
TopicInsect behavior and control techniques
Canadian institutionsCentre for Interdisciplinary Research in Music Media and Technology
Fundersnot available
KeywordsPerceptionAffect (linguistics)Identification (biology)Quality (philosophy)Noise (video)
DOInot available

Abstract

fetched live from OpenAlex

Musical sounds can be combined into timbral blends with perceptual properties that result from the overall acoustic features of the mixture.We examine the affective qualities of blended sounds.Previous studies have found that instrumental blends can have a range of distinct timbral characteristics that are different from those of the constituent sounds, which makes the perceived affects of an instrumental blend unknown and requires further research.In our experiment, 40 participants listened to 45 blended unison pairs created from 10 sustained instruments at pitch D#4.They were asked to rate the perceived affect along three dimensions (valence, tension arousal, and energy arousal).They also rated the degree of blend for each blended pair in a separate block and completed a musical sophistication questionnaire at the end of the experiment.my family and friends in China, who have been supportive and loving me.I will always honour the memory of my mentor Professor Shengchun Zhou of Morningside Scholars, who passed away last year but shed light on my life forever.

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.

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.279
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.013
GPT teacher head0.243
Teacher spread0.230 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it