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Record W2935980906 · doi:10.1119/1.5098921

Another Look at Combination Tones

2019· article· en· W2935980906 on OpenAlex
Candice Harder‐Viddal

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

VenueThe Physics Teacher · 2019
Typearticle
Languageen
FieldNeuroscience
TopicHearing, Cochlea, Tinnitus, Genetics
Canadian institutionsCanadian Mennonite University
Fundersnot available
KeywordsVariety (cybernetics)Musical acousticsAcousticsMusicalPhysicsSound waveComputer scienceArtificial intelligenceArtVisual arts

Abstract

fetched live from OpenAlex

There have been many recent articles in this journal highlighting simple demonstrations of a wide variety of acoustic phenomena. In introductory physics courses, sound waves and their propagation through air, and resonance in musical instruments, are covered in detail. However, attention is not usually paid to the active role that our ears play in transforming sound waves to create various types of combination tones. Furthermore, many physics textbooks mention that pitch is related to frequency, but do not elaborate on the specifics of the relationship. Teaching the physics behind combination tones allows for an excellent application of physics to biology, and is also an interesting way of exploring the relationship between frequency and pitch. In the first part of the paper, the physics of human hearing and the theory behind combination tones is introduced. In the second part of the paper, demonstrations are outlined and the results are presented.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
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 score0.042
Threshold uncertainty score0.996

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.004

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 head0.042
GPT teacher head0.274
Teacher spread0.232 · 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