Bibliographic record
Abstract
In an increasing number of countries, the standard treatment for deaf individuals is moving toward the implantation of two cochlear implants. Today's device technology and fitting procedure, however, appears as if the two implants would serve two independent ears and brains. Many experimental studies have demonstrated that after careful matching and balancing of left and right stimulation in controlled laboratory studies most patients have almost normal sensitivity to interaural level differences and some sensitivity to interaural time differences (ITDs). Mechanisms underlying the limited ITD sensitivity are still poorly understood and many different aspects may contribute. Recent pioneering computational approaches identified some of the functional implications the electric input imposes on the neural brainstem circuits. Simultaneously these studies have raised new questions and certainly demonstrated that further refinement of the model stages is necessary. They join the experimental study's conclusions that binaural device technology, binaural fitting, specific speech coding strategies, and binaural signal processing algorithms are obviously missing components to maximize the benefit of bilateral implantation. Within this review, the existing models of the electrically stimulated binaural system are explained, compared, and discussed from a viewpoint of a "CI device with auditory system" and from that of neurophysiological research.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".