Computational Design of Synthetic Optical Barcodes in Microdroplets
Why this work is in the frame
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Bibliographic record
Abstract
Abstract Barcodes are useful for identifying objects across time, space, and information modalities. However, materializing and decoding optical and multimodal barcodes on microscopic objects remains difficult despite the increasing need for multiplexed cell analysis. Here, a computational design of randomly combinatorial is presented, yet decodable barcodes in microdroplets. The design is based on a novel Real2Sim2Real framework: it first collects experimental images of optically distinct microparticles, then simulates massive combinatorial images by randomly assembling the imaged particles to train a neural network‐based decoder. It is demonstrated that the decoder, even though trained via simulation, accurately identifies the randomly assembled particles in real hydrogel microdroplets. It also shows that the microdroplets with an additional DNA barcoding functionality are applicable to individually link independently measured microscopic images and transcriptome profiles of pooled single cells.
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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