Total Internal Reflection Microscopy: Distortion Caused by Additive Noise
Bibliographic record
Abstract
Total internal reflection microscopy (TIRM) is an optical technique for monitoring Brownian fluctuations in the separation between a single microscopic sphere and a flat plate in aqueous media. The sphere is levitated above the plate by colloidal forces such as double-layer or steric repulsion. Changes in elevation as small as 1 nm can be detected by measuring the light scattered by a single sphere when illuminated by an evanescent wave. From the Boltzmann distribution of elevations sampled by the sphere over a long time, the potential energy (PE) profile can be determined with a resolution of about 0.1 kT . By corrupting clean data (having a mean scattering intensity of Ī s and a standard deviation of σ s ) obtained by Brownian dynamics simulations with various levels of additive background noise (having a mean background intensity of Ī b and a standard deviation of σ b ), we show how the PE profiles obtained from TIRM are distorted. Increasing Ī b narrows the profile and shifts it toward smaller elevations; conversely, increasing σ b broadens the PE profile. Subtracting Ī b from the measured total intensities before analysis removes much of the distortion. The extent of remaining distortion (broadening) depends primarily on the ratio σ b /σ s and can be neglected when this ratio is less than 0.15.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".