Bibliographic record
Abstract
The electrocardiogram (ECG) is important for several reasons. First and foremost, it provides information about the activity of the heart muscle, and it is therefore an essential non-invasive clinical tool used to diagnose certain cardiac abnormalities. From the biomechanical viewpoint, it provides a convenient reference signal for time-varying quantities in the vascular tree, specifically pulsatile flow, pressure, and arterial pulsation. Here we briefly describe the main features of the ECG. The contraction of cardiac muscle is controlled by an intrinsically generated electrical signal. In other words, the heart is responsible for its own stimulation, although the rate of stimulation can be modulated by external factors. In order to understand the behavior of this pacemaker system , we must first study several basic facts about the propagation of electric signals in excitable cells , a category that includes nerve and muscle cells. The general aspects of the description that follows are true for all excitable cells; however, the details are specific to cardiac muscle cells. Normally, the cell's interior is held at a negative electrical potential measured with respect to the surrounding extracellular fluid. This small potential difference (the resting potential , equal to approximately −90 mV) exists because of a difference in ionic composition across the cell's membrane (Table A.1). This difference in composition is actively maintained by pumps residing in the cell's membrane that transport ions against their concentration gradient.
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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 | 0.001 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.439 | 0.390 |
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".