George Ralph Mines (1886–1914): the dawn of cardiac nonlinear dynamics
Bibliographic record
Abstract
Cardiac Arrhythmias: Challenges for Diagnosis and Treatment, organized by the authors of this editorial (Fig. 1), was held at McGill University in Montreal to commemorate the centenary of the death of George Ralph Mines (Fig. 2), a Cambridge graduate who at the time of his death, on 7 November 1914 at the age of 28, was the Professor of Physiology at McGill.Inspired by Mines's seminal papers published in 1912-1914, the symposium focused on identifying areas in which basic physiology and theoretical modelling are defining new approaches to clinical cardiac electrophysiology.This special issue contains 11 papers contributed by speakers at this symposium, as well as three other contributed papers and one perspective.Despite his early death, Mines made transformational contributions to cardiac electrophysiology.His research concerning induction of tachycardia and fibrillation provides much of the basis for our current understanding of these phenomena.Mines's papers contain other insights concerning cardiac dynamics that have particular significance in light of subsequent developments in studies of cardiac electro-
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.003 | 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".