Accuracy Assessment for AG500, Electromagnetic Articulograph
Bibliographic record
Abstract
PURPOSE: The goal of this article was to evaluate the accuracy and reliability of the AG500 (Carstens Medizinelectronik, Lenglern, Germany), an electromagnetic device developed recently to register articulatory movements in three dimensions. This technology seems to have unprecedented capabilities to provide rich information about time-varying positions of articulators. However, strengths and weaknesses of the system need to be better understood before the device is used for speech research. METHOD: Evaluations of the sensor positions over time were obtained during (a) movements of the calibration device, (b) manual movements of sensors in a cartridge within the recording field of the cube, and (c) various speech tasks. RESULTS: Results showed a median error to be under 0.5 mm across different types of recordings. The maximum error often ranged between 1 and 2 mm. The magnitude of error depended somewhat on the task but largely on the location of the sensors within the recording region of the cube. CONCLUSION: The performance of the system was judged as adequate for speech movement acquisition, provided that specific steps are taken for minimizing error during recording and for validating the quality of recorded data.
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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 | 0.005 | 0.035 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".