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.
Bibliographic record
Abstract
Audio signal classification consists of extracting physical and perceptual features from a sound, and of using these features to identify into which of a set of classes the sound is most likely to fit. The feature extraction and classification algorithms used can be quite diverse depending on the classification domain of the application. This paper presents an overview of the current state of the audio signal classification research literature. 1 Introduction Audio signal classification (ASC) is a field of research that has historically been explored in a few very concentrated areas, with less work done on the general problem. The individual pieces have traditionally been speech recognition and related problems, music transcription [Moo77] [Pis86], and recently speech/music discrimination [Sau96] [SS97]. Other problems in the field have been researched as well, but the main direction has been toward speech and music applications. The major exception that has recently appeared is the...
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Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.351 | 0.002 |
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 it