Representation from the trenches: ongoing monitoring for implementing the BWC
Bibliographic record
Abstract
Advances in single-cell level profiling of the proteome require quantitative and versatile platforms, especially for rare cell analyses such as circulating tumor cell (CTC) profiling. Here we demonstrate an integrated microfluidic chip that uses magnetic nanoparticles to capture single tumor cells with high efficiency, permits on-chip incubation, and facilitates <i>in situ</i> cell-surface protein expression analysis. Combined with phage-based barcoding and next-generation sequencing technology, we were able to monitor changes in the expression of multiple surface markers stimulated in response to CTC adherence. Interestingly, we found fluctuations in the expression of Frizzled2 (FZD2) that reflected the microenvironment of the single cells. This platform has a high potential for in-depth screening of multiple surface antigens simultaneously in rare cells with single-cell resolution, which will provide further insights regarding biological heterogeneity and human disease.
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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 | 0.002 | 0.001 |
| 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".