Regular Paper: Parallel Implementation of a Cellular Automaton Modeling the Growth of Three-Dimensional Tissues
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
A promising approach for treating tissue or organ failure involves the use of bioartificial tissue substitutes grown in scaffolds with appropriate structure and shape. Currently, however, the engineering of tissue substitutes is a long and costly process based exclusively on experimentation. Predictive computer models can greatly reduce the development costs of tissue-engineered therapies by enabling scientists to rapidly evaluate the effect of system parameters on the growth rates and quality of regenerated tissues. We report here the parallel implementation of a three-dimensional model that employs cellular automata to describe the dynamic behavior of a population of mammalian cells that migrate, interact and proliferate to generate new tissues. The simulator uses MPI for interprocessor communication and is suitable for distributed memory multi-computers. Three parallel algorithms are developed to approximate the sequential algorithm describing this dynamic process of tissue growth. The parallel algorithms progressively relax the correctness requirements using different approaches to handle the cells that either move/ divide in the boundary layers of processors or cross sub-domain boundaries. Finally, a systematic study is carried out to evaluate the accuracy and performance of these algorithms.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it