Bibliographic record
Abstract
The periodicity of natural signals from our environment since the dawn of life on earth has provided the foundation for development of rhythmic responses innate to living organisms, ranging from ancient bacteria to modern humans. The evolution of organs tuned to auditory signals – especially since the colonization of terrestrial environments by early vertebrates – has been matched by the coevolution of vocalization from the production of simple warnings and alarms to complex patterns communicating sophisticated messages. The need to interpret and produce auditory information is essential for a diverse spectrum of animals. At what point does complex vocalization become “song”, and what is it that we regard as “singing”?We recognize “songs” and “singing” in the spring chorus of amphibians, the humming and chirping of insects, the species-specific songs of birds, and the complex communication of humpback whales and other cetaceans. These terms vary in meaning when describing such vocalizations compared to humans. For human singers, the act of singing and the meaning of song have strong emotional and physical components that are profound and personal, with demonstrated effects on brain activity and development, health and well-being. Is this uniquely human?This paper reviews the biophysics of hearing and the concept of singing from the perspective of evolution within the natural rhythmic soundscape of our world.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.006 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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.
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".