How to Measure the Illumination Overhead (IO) of a Fluorescence Microscope v1
Bibliographic record
Abstract
This protocol outlines in detail how to measure illumination overhead (IO) and collect raw illumination intensity data using the Rigol ds1054z oscilloscope. In fluorescence microscopy, IO is defined as the time a sample is illuminated without the emitted light being captured by the camera, resulting in unnecessary photobleaching and phototoxicity. Minimizing IO is critical for all live imaging experiments. IO occurs due to delays/errors in the synchronization between the microscope fluorescence excitation light source, exposure of the sample to excitation light and the camera exposure time for capturing emitted fluorescence light. The amount of IO varies greatly across setups due to software settings, physical hardware and how components are connected and triggered by the hardware and/or software. Thus, it is recommended that each individual measure the IO of their own microscope and experimental setup including for their exact image acquisition settings. More details use of this protocol and the need to measure IO can be found in Kiepas et. al. JCS, 2020 (https://pubmed.ncbi.nlm.nih.gov/31988150/) and Kiepas et. al. Microscopy Today, 2020 (https://www.cambridge.org/core/journals/microscopy-today/article/microscope-hardware-and-software-delays-cause-phototoxicity/B964736E4617AEC17C8B1BBD8B4F37B8)
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.049 | 0.037 |
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".