Practical approach for optimizing off-axis telecentric digital holographic microscope design
Why this work is in the frame
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Bibliographic record
Abstract
Digital holographic microscopy (DHM) has become an attractive imaging tool for the analysis of living cells and histological tissues. Telecentric DHM (TDHM) is a configuration of DHM that reduces the computational demands through a priori aberration corrections. However, TDHM requires a well-aligned optical pipeline to optimize its resolution and image quality (IQ), which has traditionally complicated the alignment process. Derived from optical interference functions, we offer here a set of methodologies to simplify TDHM design and alignment by determining the optimal +1-order position, which depends on the object-reference beam angle and the interference plane rotation angle. The methods are then experimentally tested and verified on a TDHM system by imaging living HeLa cells in suspension.
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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 | 0.001 |
| 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