Solid State High Throughput Screening Microscopy
Bibliographic record
Abstract
We introduce a solid state high throughput screening (ssHTS) imaging modality that uses a novel Newtonian telescope design to image multiple spatially separated samples without moving parts or robotics. Conventional high-throughput imaging modalities either require movement of the sample to the focal plane of the imaging system 1–3 or movement of the imaging system itself 4,5 , or use a wide-field approach to capture several samples in one frame. Schemes which move the sample or the imaging system can be mechanically complex and are inherently slow, while wide-field imaging systems have poor light collection efficiency and resolution compared to systems that image a single sample at a given time point. Our proposed ssHTS system uses a large parabolic reflector and an imaging lenses positioned at their focal distances above each sample. A fast LED array sequentially illuminate samples to generate images that are captured with a single camera placed at the focal point of the reflector. This optical configuration allows each sample to completely fill a sensors field of view. Since each LED illuminates a single sample and LED switch times are very fast, images from spatially separated samples can be captured at rates limited only by the camera’s frame rate. The system is demonstrated by imaging cardiac monolayer and Caenorhabditis elegans ( C. elegans ) preparations.
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How this classification was reachedexpand
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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".