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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".