International Test Results for Objective Lens Quality, Resolution, Spectral Accuracy and Spectral Separation for Confocal Laser Scanning Microscopes
Bibliographic record
Abstract
As part of an ongoing effort to increase image reproducibility and fidelity in addition to improving cross-instrument consistency, we have proposed using four separate instrument quality tests to augment the ones we have previously reported. These four tests assessed the following areas: (1) objective lens quality, (2) resolution, (3) accuracy of the wavelength information from spectral detectors, and (4) the accuracy and quality of spectral separation algorithms. Data were received from 55 laboratories located in 18 countries. The largest source of errors across all tests was user error which could be subdivided between failure to follow provided protocols and improper use of the microscope. This truly emphasizes the importance of proper rigorous training and diligence in performing confocal microscopy experiments and equipment evaluations. It should be noted that there was no discernible difference in quality between confocal microscope manufactures. These tests, as well as others previously reported, will help assess the quality of confocal microscopy equipment and will provide a means to track equipment performance over time. From 62 to 97% of the data sets sent in passed the various tests demonstrating the usefulness and appropriateness of these tests as part of a larger performance testing regiment.
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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 | 0.052 | 0.126 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.007 | 0.005 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".