Recording and Analysis of Currents from Single Ion Channels
Bibliographic record
Abstract
In this chapter, we will give an overview of the methods for making electrical recordings from single ion channels in cell membranes and in planar lipid bilayers, endeavoring to point out the advantages and limitations of each approach. Since extensive treatments of various aspects of the techniques are currently available, including complete books devoted to patch clamping () and to ion channel reconstitution (), we attempt to present an integrated view, with references to other discussions of details of the theory and practice that provide additional information that might be necessary to establish these techniques in the laboratory. Since the techniques of single-channel recording are being applied to an expanding range of biological problems, numerous companies are beginning to address the needs of researchers in these areas. Consequently, we will list some sources of useful equipment, software, and supplies. Finally, we will attempt to highlight current developments in instrumentation and techniques, which have appeared since other reviews were written. Of particular interest are recent approaches to presentation, analysis, and modeling of single-channel data that have just appeared or are in press at the time of writing of this article.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.005 |
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".