Noncooled Capillary Inlet: A Source of Systematic Errors in Capillary-Electrophoresis-Based Affinity Analyses
Bibliographic record
Abstract
Capillary electrophoresis (CE) serves as a platform for a large family of temperature-sensitive affinity methods. To control the electrolyte temperature, the heat generated during electrophoresis is removed by actively cooling the capillary. Short parts of the capillary, particularly at its inlet, are not actively cooled, however, and the electrolyte in this part is likely to be at an elevated temperature. Owing to their relatively short lengths, the noncooled parts have never been considered as a potential source of artifacts. Here we report for the first time that electrophoresis of the sample through the short noncooled capillary inlet can lead to large systematic errors in quantitative CE-based affinity analyses. Our findings suggest that the noncooled capillary inlet region, in spite of being short, is a source of significant artifacts that must be taken into consideration by developers and users of CE-based affinity methods. We propose a simple solution for this problem: moving the sample through the noncooled inlet into the cooled region by pressure or by a low-strength electric field to save it from exposure to the elevated temperature.
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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 | 0.015 | 0.040 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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".