Integrated self-calibrationvia electrokinetic solvent proportioning for microfluidic immunoassays
Bibliographic record
Abstract
On-board generation of a set of calibration standards was demonstrated within a microfluidic device designed to perform immunoassay. Electrokinetic flow was used to proportionally mix the antibody (Ab) to bovine serum albumin (BSA) and a diluting buffer, to provide varying Ab concentrations for downstream mixing with fluorescently labeled BSA (BSA*). Mixing ratios were determined from electrical impedance modeling of the fluidic network using P-SPICE software, and peak heights for the labeled species were analyzed relative to the concentration calculated from the model. For dilution and separation of fluorescently labeled amino acids, a linear calibration curve was obtained for mixing ratios of 0.118 to 7.46. A linear calibration curve was obtained for the immunoassay calibration using dilution ratios between 0.197 and 5.077. Deviations were observed at larger extremes, possibly due to leakage effects at intersections. Peak height reproducibility was +/- 3% for the immunoassay, using diluted monoclonal Ab in mouse ascites fluid as the analyte. Recovery for on-chip calibration was 92 +/- 6% versus calibrants prepared off-chip, indicating a small bias.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".