A software platform to administer the Canadian Digit Triplet Test
Bibliographic record
Abstract
Originally designed in Dutch as an automatic self-screening test (Smits et al., IJA 43(1):15–28, 2004), the Digit Triplet Test has been developed in about 15 different languages. Owing to its wide success, and to facilitate the comparison between languages, a working group on multilingual speech testing of the International Collegium of Rehabilitative Audiology (ICRA) has provided recommendations for constructing such tests (Akeroyd et al., IJA 54 Suppl 2:17-22, 2015). The development of the Canadian-English and Canadian-French versions of the Digit Triplet Test includes preparing speech and noise materials and implementing a testing platform. The digits were recorded in both languages by two fluently bilingual adult talkers (1 male, 1 female). The recordings were processed and optimized and a speech-shaped noise signal was developed for each language-talker combination according to the ICRA recommendations and ISO standard on speech audiometry (ISO 8253-3:2012). The present work describes the software interface of the testing platform, the supported features as well as the required hardware components. The test will be deployed in large-scale multi-site longitudinal studies across Canada on aging and neurodegeneration in aging. In addition, the test may be a useful tool for lab-based research, the audiologic assessment of francophone speakers in minority settings in Canada, and for studies of English or French as a second language.
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How this classification was reachedexpand
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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.001 |
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 teacher head, 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".