Dual-Modality Imaging Microfluidic Cytometer for Onsite Detection of Phytoplankton
Bibliographic record
Abstract
Phytoplankton monitoring is essential for better understanding and mitigation of phytoplankton bloom formation. We present a microfluidic cytometer with two imaging modalities for onsite detection and identification of phytoplankton: a lensless imaging mode for morphological features, and a fluorescence imaging mode for autofluorescence signal of phytoplankton. Both imaging modes are integrated in a microfluidic device with a field of view (FoV) of 3.7 mm × 2.4 mm and a depth of field (DoF) of 0.8 mm. The particles in the water flow channel can be detected and classified with automated image processing algorithms and machine learning models using their morphology and fluorescence features. The performance of the device was demonstrated by measuring Chlamydomonas, Euglena, and non-fluorescent beads in both separate and mixed flow samples. The recall rates for Chlamydomonas and Euglena ware 93.6% and 94.4%. The dual-modality imaging approach enabled observing both morphology and fluorescence features with a large DoF and FoV which contribute to high-throughput analysis. Moreover, this imaging flow cytometer platform is portable, low-cost, and shows potential in the onsite phytoplankton monitoring.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".