Bibliographic record
Abstract
Computer models have been widely used to numerically simulate the physical dynamics of vocal fold oscillation in phonation. Computer simulations have allowed the exploration of a much wider parameter space and a much longer time scale than what would be very costly or sometimes impossible with animal and human models. In the past 10 years, our team uses a combined in vivo, in vitro, and in silico approach to understand the biological mechanism underlying the formation of vocal lesions. The ultimate goal is to identify key biological and physical factors associated with the pathogenesis of and recovery from vocal fold injury that will be useful for personalized voice restoration. Tissue injury and repair is a complex process that involves multiple cells types and molecular signals. We developed 3D agent-based models at a physiological scale to simulate and visualize the cellular and molecular response to vocal injury and treatment. Significant progress has made to improve the model’s computational speed and visualization features using our hybrid CPU-GPU computing platform. Gaps however still exist between the model and the reality. In this talk, I will present what we have achieved so far as well as what challenges we are facing in biological computing.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 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".