A reliability study of square wave bursting $\beta$-cells with noise
Bibliographic record
Abstract
Reliability of spike timing has been a hot topic recently. Howeverreliability has not been considered for bursting behavior, ascommonly observed in a variety of nerve and endocrine cells,including $\beta$-cells in intact pancreatic islets. In this paper,reliability of $\beta$-cells with noise is considered. A method tonumerically study reliability of bursting cells is presented.Reliability of a single cell will decrease as noise level becomeslarger. The reliability of networks of $\beta$-cells coupled by gapjunctions or synaptic excitation is investigated. Simulations of thenetwork of $\beta$-cells reveal that increasing noise leveldecreases the reliability. But the reliability of the network ishigher than that of single cell. The effect of coupling strength onreliability is also investigated. Reliability will decrease whencoupling strength is small and increase when coupling strength islarge.
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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 | 0.001 | 0.004 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".