Semi-automated detection of single cell signatures from a dielectrophoretic cytometer
Bibliographic record
Abstract
We present a semi-automated event identification method for collecting the dielectrophoretic signatures of cells flowing through a dieletrophoretic cytometer. The marker free dielectrophoresis (DEP) cytometer presented in this study is capable of detecting electronic signatures of cells which identifies Claussius-Mossotti factor (CMF). The CMF can in turn be used to determine properties of the cell such as the viability. In past work the DEP cytometer signals were manually sorted by going through the entire recorded signals, which is very time-consuming. In the semi-automated method of collection, events are identified and displayed in the user interface to be accepted or rejected. We present results using semi-automated method on Hamster Chinese Ovary (CHO) cells in a batch culture and compared them with the manual analysis. The automated approach identified over 80% of the events identified manually and produced event histogram distributions nearly identical to the manual method.
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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) | 0.000 | 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 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".