Any Way You Slice It—A Comparison of Confocal Microscopy Techniques
Bibliographic record
Abstract
The confocal fluorescence microscope has become a popular tool for life sciences researchers, primarily because of its ability to remove blur from outside of the focal plane of the image. Several different kinds of confocal microscopes have been developed, each with advantages and disadvantages. This article will cover the grid confocal, classic confocal laser-scanning microscope (CLSM), the resonant scanning-CLSM, and the spinning-disk confocal microscope. The way each microscope technique works, the best applications the technique is suited for, the limitations of the technique, and new developments for each technology will be presented. Researchers who have access to a range of different confocal microscopes (e.g., through a local core facility) should find this paper helpful for choosing the best confocal technology for specific imaging applications. Others with funding to purchase an instrument should find the article helpful in deciding which technology is ideal for their area of research.
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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 | 0.006 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".