Bibliographic record
Abstract
This chapter contains sections titled: Introduction Magnetoencephalography Introduction MEG Signals Whole-Cortex MEG Systems Preface MEG System Description MEG and EEG MEG Peripherals Sensor Types for MEG Introduction Baseline Optimization for Radial Devices Radial and Planar Gradiometers Radial and Vector Magnetometers Comparison of Various Devices in the Presence of Environmental Noise Fetal MEG Systems Introduction Design Philosophy Construction of fMEG System MEG Data Analysis General Principles MEG Data Interpretation (MEG Source Imaging) Examples of MEG and fMEG Results Clinical MEG Conclusions and Outlook Magnetocardiography Introduction Sources of MCG Signals Cardiomagnetic Instrumentation General System Description and Sensor Array Configurations Multichannel Low-Temperature Systems Systems Operating Outside Magnetically Shielded Rooms High-Temperature SQUIDs Multichannel High-Tc Systems Comparisons of Sensor Types Sensitivity Axial and Planar Sensors Vector Magnetometers Conversion of Signals from Different Sensor Arrays Applications of MCG Introduction Arrhythmia Risk Stratification Studies on Myocardial Ischemia Cardiac Source Imaging Fetal MCG Discussion Statistical Validation of MCG Results Improvement of Clinical Applicability Conclusion and Outlook Quasistatic Field Magnetometry Opening the Near-dc Window Quasistatic Field Magnetometer Applications Magnetoneurography The Long History of Measuring Signal Propagation in Nerves Measurement Technique and Signal Processing Source Modeling for Magnetoneurography Clinical Perspective Liver Susceptometry Incorporated Magnetism as a Source of Diagnostic Information Measurement Technique Applications Outlook Gastromagnetometry Magnetogastrography (MGG) and Magnetoenterography (MENG) Magnetic Marker Monitoring (MMM) Magnetic Relaxation Immunoassays Immunoassays and Superparamagnetic Particles MARIA Instrumental Developments Applications
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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 | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.159 | 0.049 |
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".