Quantifying bio-filament morphology below the diffraction limit of an optical microscope using out-of-focus images
Bibliographic record
Abstract
A method to measure the dimensions of objects below the optical diffraction limit using diffraction analysis of out-of-focus bright-field images is presented. The method relies on the comparison of the diffraction patterns of an object of unknown size to those of calibration objects of known size. Correlative scanning electron microscope measurements are used to demonstrate the applicability of this method to measure 100 nm microbeads as well as objects with a geometry different from the calibration objects. This technique is important in the context of tethered particle experiments, in which bio-filaments are bound between a substrate and a microbead. This procedure is applied to obtain the diameters of axonal extensions or neurites that are mechanically created in samples of rat hippocampal neurons. The dependence of neurite geometry on mechanical pull speed is investigated, and the diameter is found to be rate independent.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".