Scanning Temperature Gradient Focusing
Bibliographic record
Abstract
Temperature gradient focusing (TGF) is a recently developed technique for the simultaneous concentration and electrophoretic separation of ionic analytes in microfluidic channels. One drawback to TGF as it has previously been described is the limited peak capacity; only a small number of analyte peaks (approximately 2-3) can be simultaneously focused and separated. In this paper, we report on a variation of the TGF method whereby the bulk flow rate is varied over time so that a large number of analytes can be sequentially focused, moved past a fixed detection point, and flushed to waste. In addition to improved peak capacity, the detection limits of the scanning TGF method can be adjusted on-the-fly, as needed for different samples. Finally, scanning TGF provides a technique by which high-resolution, high-peak-capacity electrophoretic separations can be performed in simple, straight, and short microfluidic channels.
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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".