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
Tabletop computers (also known as surface computers and smart tables) have been growing in popularity for the past decade and are poised to make inroads into the consumer market, opening up a new market for the games industry. But before tabletop computers become widely accepted, there are many questions with respect to sound production and reception for these devices that need to be explored, particularly when it comes to multimedia consumption on the devices. For example, which loudspeaker setups should be used to take into consideration the multi-user nature of tabletop computers, and which panning method(s) maximize the spatial localization abilities of the user(s)? Previous work suggests that a quadraphonic diamond-shaped loudspeaker configuration—whereby a loudspeaker is placed at each of the four sides of the tabletop computer—leads to more accurate localization results when compared with a traditional quadraphonic loudspeaker configuration—whereby a loudspeaker is placed at each of the four corners of the tabletop computer. Given this preference for a diamond loudspeaker configuration, we examine two amplitude-panning methods (bilinear interpolation and inverse distance) for spatializing a sound on the (horizontal) surface of the table-computer with a diamond loudspeaker configuration. Results from the study detailed in this paper indicate that there are no significant differences between the two methods and that both methods are prone to error.
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 | 0.000 | 0.000 |
| 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