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 SoundCraft, a smartwatch prototype embedded with a microphone array, that localizes angularly, in azimuth and elevation, acoustic signatures: non-vocal acoustics that are produced using our hands. Acoustic signatures are common in our daily lives, such as when snapping or rubbing our fingers, tapping on objects or even when using an auxiliary object to generate the sound. We demonstrate that we can capture and leverage the spatial location of such naturally occurring acoustics using our prototype. We describe our algorithm, which we adopt from the MUltiple SIgnal Classification (MUSIC) technique [31], that enables robust localization and classification of the acoustics when the microphones are required to be placed at close proximity. SoundCraft enables a rich set of spatial interaction techniques, including quick access to smartwatch content, rapid command invocation, in-situ sketching, and also multi-user around device interaction. Via a series of user studies, we validate SoundCraft's localization and classification capabilities in non-noisy and noisy environments.
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 | 0.000 | 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 | 0.001 | 0.001 |
| Open science | 0.001 | 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