An Automated Microfluidic System for Morphological Measurement and Size-Based Sorting of <i>C. Elegans</i>
Bibliographic record
Abstract
This paper reports a vision-based automated microfluidic system for morphological measurement and size-based sorting of the nematode worm C. elegans. Exceeding the capabilities of conventional worm sorting microfluidic devices purely relying on passive sorting mechanisms, our system is capable of accurate measurement of the worm length/width and active sorting of worms with the desired sizes from a mixture of worms with different body sizes. This function is realized based on the combination of real-time, vision-based worm detection and sizing algorithms and automated on-chip worm manipulation. A double-layer microfluidic device with computer-controlled pneumatic valves is developed for sequential loading, trapping, vision-based sizing, and sorting of single worms. To keep the system operation robust, vision-based algorithms on detecting multi-worm loading and worm sizing failure have also been developed. We conducted sorting experiments on 319 worms and achieved an average sorting speed of 10.4 worms per minute (5.8 s/worm) with an operation success rate of 90.3%. This system will facilitate the worm biology studies where body size measurement and size-based sorting of many worms are needed.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".