Tissue-Like 3D Standard and Protocols for Microscope Quality Management
Bibliographic record
Abstract
ABSTRACT This article outlines a global study conducted by the Association of Biomedical Resource Facilities (ABRF) Light Microscopy Research Group (LMRG). The results present a novel 3D tissue-like biologically relevant standard sample that is affordable and straightforward to prepare. Detailed sample preparation and instrument specific image acquisition protocols and image analysis methods are presented and made available to the community. The standard consists of sub-resolution and large well characterized relative intensity fluorescence microspheres embedded in a 120 µm thick 3D gel with a refractive index of 1.365. The standard allows the evaluation of several properties as a function of depth. These include: 1) microscope resolution with automated analysis of the point spread function (PSF), 2) automated signal-to-noise- ratio analysis, 3) calibration and correction of fluorescence intensity loss, and 4) quantitative relative intensity. Results demonstrate expected refractive index mismatch dependent losses in intensity and resolution with depth but the relative intensities of different objects at similar depths were maintained. This is a robust standard showing reproducible results across laboratories, microscope manufacturers and objective lens types (e.g. magnification, immersion medium). Thus, these tools will be valuable for the global community to benchmark fluorescence microscopes and will contribute to improved rigor and reproducibility.
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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 | 0.015 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 0.009 |
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".