Bibliographic record
Abstract
Starting in 2002 John and Martina McBride set out to create several small rooms, each one unique in design and technology. George Massenburg (GM) was asked to coordinate the design of one of the rooms and given the mandate to make it the most advanced room imaginable. GM specified that the room should offer an accurate monitoring environment for more than one listener, a reasonable presentation of how materials will sound in multiple circumstances outside of the space, a flexible environment that doesn’t compromise musical and artistic contexts, an accurate representation of virtual sources (sources spread across one or more loudspeakers) to more than one listener, a room with linear, supportive ambience, which as much as possible, has near-equal decay rates across as much of the frequency spectrum as possible. In June 2004, GM contacted Peter D’Antonio with a proposal to collaborate on a new surround music monitoring/control/recording room, which became Studio C. The resulting diffusor design covering all four walls and ceiling, with corner bass absorption, will be presented. Since opening the room has been used successfully and has recently installed a Dolby Atmos system. The presentation will also present user perceptions from 2005 to the present.
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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.060 | 0.029 |
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".