Bibliographic record
Abstract
Recent evidence indicates that the majority of occasional singers can carry a tune. For example, when asked to sing a well-known song (e.g., "Happy Birthday"), nonmusicians performing at a slow tempo are as proficient as professional singers. Yet, some occasional singers are poor singers, mostly in the pitch domain, and sometimes despite not having impoverished perception. Poor singing is not a monolithic deficit, but is likely to be characterized by a diversity of singing "phenotypes." Here we systematically examined singing proficiency in a group of occasional singers, with the goal of characterizing the different patterns of poor singing. Participants sang three well-known melodies (e.g., "Jingle Bells") at a natural tempo and at a slow tempo, as indicated by a metronome. For each rendition, we computed objective measures of pitch and time accuracy with an acoustical method. The results confirmed previous observations that the majority of occasional singers can sing in tune and in time. Moreover, singing at a slow tempo after the target melody to be imitated was presented with a metronome improved pitch and time accuracy. In general, poor singers were mostly impaired on the pitch dimension, although various patterns of impairment emerged. Pitch accuracy or time accuracy could be selectively impaired; moreover, absolute measures of singing proficiency (pitch or tempo transposition) dissociated from relative measures of proficiency (pitch intervals, relative duration). These patterns of dissociations point to a multicomponent system underlying proficient singing that fractionates as a result of a developmental anomaly.
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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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 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".