Bibliographic record
Abstract
Issues concerning auditory scene analysis (ASA) raised by the previous speakers will be discussed: (1) Disorders of ASA in humans can tell us about the weighting of cues in ASA. (2) The apparent weakness of spatial cues for ASA may simply show that they interact strongly with other ASA cues (c.f., recent research in the author’s lab). (3) The power of harmonic relations among partials as a grouping cue is not guaranteed, but depends on many other factors. (4) Abstract models of ASA may require the peripheral auditory system to carry out analyses that are questionable, based on current psychophysical and physiological findings. Is this where psychologists and computational ASA (CASA) modelers part company? (5) The ‘‘old-plus-new heuristic,’’ one of the most potent ASA mechanisms, is neglected by existing CASA models. (6) The different roles of bottom-up and top-down processes (e.g., in ‘‘exclusive allocation’’ of sensory evidence) should be reflected in models. (7) Should the output of a CASA system be the reconstructed signal of a single source, as a front end to a recognition system, or should grouping mechanisms merely form an interacting part of a larger system that outputs a higher-level description (e.g., a series of words)?
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.
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 | 0.030 | 0.075 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.002 | 0.023 |
| Scholarly communication | 0.017 | 0.028 |
| Open science | 0.008 | 0.007 |
| Research integrity | 0.006 | 0.006 |
| Insufficient payload (model declined to judge) | 0.005 | 0.003 |
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".