Bibliographic record
Abstract
Research on auditory scene analysis (ASA) began with some simple laboratory phenomena such as streaming and illusory continuity. Subsequently, research has gone in three directions, downwards toward underlying mechanisms (by neurophysiologists), upwards toward system organization (by computer scientists), and sideways toward other species (by neurobiologists). Each direction has its problems. The downward approach sometimes takes a phenomenon-oriented view of ASA, leading to simple explanations of a single ASA demonstration, such as streaming, with no obvious connection to any larger system. Research done by the upward approach usually takes the form of a computer program to achieve ASA in a working system, often ignoring known facts about human ASA, in favor of mathematically understood principles. The sideways approach often finds that non-human animals can respond to an important sound despite the presence of other interfering sounds. However, there is no reason to believe that a frog, a fish, and a human accomplish this by means of the same mechanisms. So finding out how some animal does this, while interesting in its own right, may shed little light on how humans do it. I will describe some properties of the human ASA system that should be borne in mind when manufacturing explanations.
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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 | 0.020 | 0.016 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.008 | 0.006 |
| Science and technology studies | 0.004 | 0.020 |
| Scholarly communication | 0.012 | 0.026 |
| Open science | 0.004 | 0.007 |
| Research integrity | 0.009 | 0.014 |
| Insufficient payload (model declined to judge) | 0.012 | 0.004 |
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.
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".