Fluorescence Microscopy Light Source Review
Why this work is in the frame
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Bibliographic record
Abstract
Traditional arc-based light sources and Light Emitting Diodes (LEDs) are described, and the pros and cons of these sources with respect to fluorescence microscopy are discussed. For multi-color applications, arc-based light sources offer white light ranging from the ultraviolet (UV) to the infrared (IR), while LEDs come in a range of colors spanning the same wavelengths. The power of traditional arc-based sources is controlled with neutral density (ND) filters, reducing power across the entire range of wavelengths, while LED-based sources can be controlled directly by modulating current passing through the electronics. Similarly, arc-based sources use physical shutters to control sample exposure to light in a range of tens to hundreds of milliseconds (ms), while LEDs can be turned ON/OFF electronically in <1 ms. The complexity of comparing and measuring light power on the sample, due to normalization of available light source spectra and complex power measurements, is discussed. The superiority of LEDs for stability of light power output is covered. Direct coupling of light sources to the microscope is more cost effective and leads to higher available light power. Various options for setting up multi-color imaging, including high-speed imaging with multiple LEDs and a triple cube, are described. A brief introduction to lasers, with suggested further reading, is included in this article. Finally, the smaller environmental footprint of LEDs relative to arc-based light sources is highlighted. © 2021 Wiley Periodicals LLC.
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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 | 0.000 | 0.000 |
| 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 | 0.000 |
| 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 it