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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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.030 | 0.031 |
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".