A Novel Edge Effect Detection Method for Real-Time Cellular Analyzer Using Functional Principal Component Analysis
Bibliographic record
Abstract
Real-time cellular analyzer (RTCA) has been generally applied to test the cytotoxicity of chemicals. However, several factors impact the experimental quality. A non-negligible factor is the abnormal time-dependent cellular response curves (TCRCs) of the wells located at the edge of the E-plate which is defined as edge effect. In this paper, a novel statistical analysis is proposed to detect the edge effect. First, TCRCs are considered as observations of a random variable in a functional space. Then, functional principal component analysis (FPCA) is adopted to extract the principal component (PC) functions of the TCRCs, and the first and second PCs of these curves are selected to distinguish abnormal TCRCs. The average TCRC of the inner wells with the same culture environment is set as the standard. If the distance between the scoring point of the standard curve and one designated scoring point exceeds the defined threshold, the corresponding TCRC of the designated point should be removed automatically. The experimental results demonstrate the effectiveness of the proposed algorithm. This method can be used as a standard method to resolve general time-dependent series issues.
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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 | 0.001 | 0.001 |
| 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".