A system for developing a series of interactive tests of vocal production requiring on-line audiovisual recording
Bibliographic record
Abstract
AIRS-TEST, an online system supporting a major collaborative research initiative, Advancing Interdisciplinary Research in Singing (AIRS), was developed. AIRS-TEST administers a sequence of interactive tests and organizes the results for analysis. The tests can present text and audiovisual information to prompt the participant's response (e.g., key presses, mouse clicks, touch-screen or audiovideo input). Researchers can design and create a sequence of related tests with auditory and/or visual stimuli via a management interface delivered by a web browser. Audiovideo recording modules can be embedded into the tests in many flexible ways. Participants need an invitation code to access a test collection. Experimental results can be explored online or downloaded for further analysis. An authority module is associated with collected data to control user's right of retrieval, considering both confidentiality and collaborative sharing. The software technologies supporting the various modules of AIRS-TEST are MySQL, Java EE, Flex and Red5. Whereas AIRS-TEST will be used worldwide to promote the AIRS study of cultural, universal, and individual influences on the acquisition of singing (Cohen et al, 2009, Annals NYAS, 1169, 112-115), AIRS-TEST can potentially support other experiments requiring on-line audio or audiovisual recording, as will be demonstrated. [Work supported by SSHRC MCRI]
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.049 | 0.018 |
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".