A simple, inexpensive and multi‐scale 3‐D fluorescent test sample for optical sectioning microscopies
Bibliographic record
Abstract
Abstract Fluorescence standards allow for quality control and for the comparison of data sets across instruments and laboratories in applications of quantitative fluorescence. For example, users of microscopy core facilities can expect a homogenous and time‐invariant illumination and an uniform detection sensitivity, which are prerequisites for imaging analysis, tracking or fluorimetric pH or Ca 2+ ‐concentration measurements. Similarly, confirming the three‐dimensional (3‐D) resolution of optical sectioning microscopes calls for a regular calibration with a standardized point source. The test samples required for such measurements are typically different ones, they are often expensive and they depend much on the very microscope technique used. Similarly, the ever‐increasing choice among microscope techniques and geometries increases the demand for comparison across instruments. Here, we advocate and demonstrate the multiple uses of a surprisingly versatile and simple 3‐D test sample that can complement existing and much more expensive calibration samples: commercial tissue paper labeled with a fluorescent highlighter pen. We provide relevant sample characteristics and show examples ranging from the sub‐μm to cm scale, acquired on epifluorescence, confocal, image scanning, two‐photon (2P) and light‐sheet microscopes.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| 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) | 0.006 | 0.003 |
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".