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
We present a technique to facilitate the creation of constantly changing, randomized audio streams from samples of source material. A core motivation is to make it easier to quickly create soundscapes for virtual environments and other scenarios where long streams of audio are used. While mostly in the background, these streams are vital for the creation of mood and realism in these types of applications. Our approach is to extract the component parts of sampled audio signals, and use them to resynthesize a continuous audio stream of indeterminate length. An automatic segmentation algorithm involving wavelets is used to split the input signal into syllable-like audio segments that we call “natural grains.” For each grain, a table of similarity between it and all the other grains is constructed. The grains are then output in a continuous stream, with the next grain being chosen from among those other grains which best follow from it. Using this sampling-resynthesis technique, we can construct an infinite number of variations on the original signal with a minimum amount of interaction. An interface for the manipulation and playback of several of these streams is provided to facilitate building complex audio environments, and is made available for online experimentation at www.cs.ubc.ca/labs/lci/naturalgrains/ .
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.
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 | 0.001 | 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it