Parsing contributions of physical phenomena to smFRET statistical inhomogeneity via multiparameter stochastic simulations
Bibliographic record
Abstract
Single-molecule Förster Resonance Energy Transfer (smFRET) affords access to nanometre-scale structural and kinetic information for individual biomolecular species. Conventional analyses presuppose a strict separation of the underlying dynamical processes into distinct timescales - an assumption that is frequently violated and seldom verifiable a posteriori . To address this limitation, we present an integrated Brownian dynamics/stochastic simulation framework that treats the three principal dynamic contributors to smFRET observables - (i) diffusion of the molecule’s centre of mass, (ii) photophysical state-cycling, and (iii) intramolecular diffusion - in a fully time-resolved manner. Each contribution can be selectively activated, deactivated, and parametrically adjusted, thereby providing a controlled computational testbed for determining the extent to which distinct dynamical contributions alter smFRET data. By systematically varying these contributions, the individual and collective impact of specific physical processes on smFRET measurements can be delineated, and therefore the biologically-relevant information (iii) can be more precisely estimated.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".