Noise in combined optical microscopy and dynamic force spectroscopy: Toward <i>in vivo</i> hydration measurements
Bibliographic record
Abstract
Noncontact atomic force microscopy (AFM) using frequency modulation (FM) detection allows atomic resolution to be obtained in vacuum on a variety of insulating surfaces and molecular deposits. This technique has recently been extended to liquid environments, and, in addition to atomic scale contrast, FM-AFM in liquid allows measurement of ordered liquid layers above surfaces. The role of water and ions in biological processes is of great interest and in order to localize fluorescently tagged structures, such as proteins, optical microscopy combined with AFM provides an invaluable tool. Thus, to take advantage of the wealth of optical identification techniques available in biology, the AFM must be coupled to an optical microscope. Such systems are commercially available, but mechanical noise due to vibrations is a major concern compared with the compact, specialized instruments used to measure hydration structure to date. In this article the authors demonstrate, through both modeling and measurement, that hydration structure can be measured on such a commercial “bio-AFM,” despite the additional noise sources present in these instruments and that with the addition of a bandpass filter and amplifier it can be done “out-of-the-box” using only commercial electronics and tips. Thus, hydration structure measurements are accessible to virtually any laboratory with such a system.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| 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".