Bibliographic record
Abstract
The muscle cell or neuron membrane is functionally equivalent to a resistor-capacitor (RC) circuit with the membrane resistance and capacitor in parallel. Once inserted inside the membrane, an electrode introduces a serial resistance and small capacitance to the RC circuit. Through a narrow opening at its tip (∼0.1-μm), current can pass through the electrode, into the cell, and back to the outside (ground) across the membrane to complete the circuit. This arrangement enables a voltage difference between the outside and inside of the cell membrane to be recorded. To determine cell membrane properties, a current can be injected into the cell through the electrode. One complication with this approach, however, is that the voltage difference measured with the electrode includes the voltage drop across the cell membrane and that across the electrode. Furthermore, a small amount of current is drawn by the electrode capacitor, thereby slowing the current flow across the membrane. Fortunately, most amplifiers are equipped with bridge balance and capacitance compensation functions so that the effects of the electrode on cell membrane properties can be canceled out or minimized. This protocol describes the basics of setting up and conducting electrophysiological experiments using a model cell. For the novice, a model cell provides a way to learn the operation of electrophysiology equipment and software without the anxiety of damaging living cells. This protocol also illustrates passive membrane properties such as the input resistance, capacitance, and time constant.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.003 |
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".