Light Microscopy Research Group (LMRG): International Test Results for Objective Lens Quality, Resolution, Spectral Accuracy and Spectral Separation for Confocal Laser Scanning Microscopes (CLSM)
Bibliographic record
Abstract
As part of an ongoing effort to increase image reproducibility and fidelity in addition to improving cross-instrument consistency we have developed four separate instrument quality tests to augment the ones previously reported. 1) Objective lens quality 2) Resolution 3) Spectral accuracy of the wavelength information from spectral detectors 4) Spectral Separation tested the accuracy and quality of un-mixing algorithms. To ascertain the usefulness of these tests as well as to determine the current ““state”” of microscopes in use, test specimens and detailed protocols were made available worldwide free-of-charge. 55 laboratories located in 18 countries provided data. Objective lens quality: good with most issues arising from user errors or stage/focus drift, approximately 10% of lenses had aberrations. Resolution: within an average of approximately 25% of theoretical values. Spectral accuracy: excellent, even for low resolution systems. Spectral un-mixing: good, poor data collection was the main cause of low quality data.
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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 | 0.001 | 0.002 |
| 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 | 0.001 |
| 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".